Several approaches are exists today according to color, intensity and saturation value etc that are very less accurate. Within this paper, we advise alternative shadow recognition formula according to thresholding and morphological filtering, along with an alternate shadow renovation formula in line with the example learning method and Markov random field (MRF). The primary purpose of this project is recognition and renovation of shadows from VHSR images. Removing or alleviating the instants while using shadows in HSR images for more processing is an extremely important task because the shadows are induce to loss or miss conjecture of radiometric information and induce to image interpretation. Throughout the shadow recognition procedure, the bimodal distributions of pixel values within the near-infrared (NIR) band and also the panchromatic band are adopted for thresholding. Throughout the shadow renovation procedure, we model the connection between non shadow and also the corresponding shadow pixels and between neighboring no shadow pixels by using MRF. With extension for this paper we advise Bayesian conjecture way of accurate conjecture of shadow. Within this paper for accurate shadow recognition we combine thresholding and morphological filtering concepts. This shadow recognition includes Thresholding, Morphological filtering and edge compensation stages

Alma Villaseñor Franco

Emilio Alvarez_Parrilla

Gustavo A. González-Aguilar

Jesus Fernando Ayala Zavala

Joaquin Rodrigo-Garcia

José Núñez-Gastélum

Laura Alejandra Ardilla De La Rosa Carrillo

Wall_Medrano Abraham

Nut by-products are the materials discarded from the peeling process of
edible peanuts and tree nuts such as almonds, pistachio, pecans, and hazelnuts.
These by-products constitute more than 50% of the total weight generated
by the nut industry and represent a large amount of solid waste to handle
by the producers. Currently, few useful strategies, with low or nule-added
value, exist for disposal of these by-products. Some authors point out that
there are more ambitious applications for their uses. They are an excellent
source of phenolic compounds and dietary fiber with potential applications,
not only as nutraceuticals or functional food ingredients to protect human
health but also to enhance shelf life or quality of several foodstuffs when
used as natural additives. The aim of this chapter is to review the nutraceutical
potential of phytochemicals from nut by-products, their physiological
effects in human health, and their use in the food industr

Repositorio Institucional de la UACJ

Byproducts of the nut and peanut agro-industry as sources of phytochemicals suitable for the nutraceutical and food industries

https://www.crcpress.com/Plant-Food-By-Products-Industrial-Relevance-for-Food-Additives-and-Nutraceuticals/Ayala-Zavala-Gonzalez-Aguilar-Siddiqui/p/book/978177188640

Laura A. de la Rosa

By-products of the nut and peanut agro-industry as sources of phytochemicals suitable for the nutraceutical and food industries

Manasa, M.

Vijendra Kumar, D.

International Journal of Innovative Technology and Research (IJITR)

M. Manasa* et al.   (IJITR) INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND RESEARCH   Volume No.4, Issue No.6, October – November 2016, 4803-4806.  2320 –5547 @ 2013-2016 http://www.ijitr.com All rights Reserved.  Page | 4803 Bayesian Prediction Method For Shadow Detection And Reconstruction In HSR Images Using Morphological Filter M.MANASA Department of ECE, Godavari Institute of Engineering and Technology Rajahmundry D.VIJENDRA KUMAR Assistant Professor Department of ECE, Godavari Institute of Engineering and Technology RajahmundryAbstract: Several approaches are exists today according to color, intensity and saturation value etc that are very less accurate. Within this paper, we advise alternative shadow recognition formula according to thresholding and morphological filtering, along with an alternate shadow renovation formula in line with the example learning method and Markov random field (MRF). The primary purpose of this project is recognition and renovation of shadows from VHSR images. Removing or alleviating the instants while using shadows in HSR images for more processing is an extremely important task because the shadows are induce to loss or miss conjecture of radiometric information and induce to image interpretation. Throughout the shadow recognition procedure, the bimodal distributions of pixel values within the near-infrared (NIR) band and also the panchromatic band are adopted for thresholding. Throughout the shadow renovation procedure, we model the connection between non shadow and also the corresponding shadow pixels and between neighboring no shadow pixels by using MRF. With extension for this paper we advise Bayesian conjecture way of accurate conjecture of shadow. Within this paper for accurate shadow recognition we combine thresholding and morphological filtering concepts. This shadow recognition includes Thresholding, Morphological filtering and edge compensation stages. Keywords: Markov Random Field (MRF); Shadow Detection; Near-Infrared; Image Shadows; Prediction Information; I. INTRODUCTION The pictures acquired from satellites have high spatial resolution (VHSR), usually ranged from .5 to 4 m. Only at that resolution, details for example structures along with other infrastructures are often visible. Therefore, these VHSR images have opened up a brand new era for remote sensing applications, for example object recognition, classification, object mapping, and alter recognition. Generally, two steps take part in this process: Shadow recognition and shadow renovation (compensation). Regarding shadow recognition in VHSR images, two primary approaches are reported in the last literature, namely, the model-based and also the property-based [1]. Particularly, VHSR images have attracted much attention from researchers studying cities, because of the information on relatively small features, for example roads, structures, bridges, and trees. Inevitably, tall standing objects of these small features cast lengthy shadows in the majority of the taken VHSR images. Around the one hands, these shadows might be utilized like a valuable cue for inferring 3-D scene information according to their position and shape, for instance, for building recognition and building height estimation. However, the shadows cause partial or total lack of radiometric information within the impacted areas, and therefore, they create tasks like image interpretation, object recognition and recognition, and alter recognition harder or perhaps impossible. Within this paper, we concentrate on the second facet of shadows, i.e., to attenuate the issues brought on by losing radiometric information in shadowed areas by paying or reconstructing them. The previous requires prior understanding of scene or sensors, including, although not restricted to, distribution of scene radiance and acquisition parameters like sun azimuth, sensor/camera localization, date, and also the time of acquisition. The suggested shadow recognition method in first transformed the RGB image into HSV space after which derived a normalized saturation-value difference index (NSVDI) to recognize shadows via thresholding. Several photometric invariant color models for shadow recognition were compared in. In the area-growing-based methods, the seed points are first selected, after which, each one of the pixels is owned by a segment based on their distance from individuals regions that they might potentially be assigned. The authors in suggested a straight line regression approach to bridge no shadow and shadow areas for every class in every band [2]. Lately, another straight line-regression-based way of shadow renovation continues to be suggested in, which assumed that both shadow and no shadow pixels of every class consume a Gaussian distribution after which solved the straight line regression parameters through the parametric M. Manasa* et al.   (IJITR) INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND RESEARCH   Volume No.4, Issue No.6, October – November 2016, 4803-4806.  2320 –5547 @ 2013-2016 http://www.ijitr.com All rights Reserved.  Page | 4804 estimation method. Within the shadow renovation method suggested within this paper, an identical ground-truth collection procedure is going to be adopted but with no classification step. Within this paper, we advise an alternate shadow recognition formula according to thresholding and morphological filtering, along with an alternate shadow renovation formula in line with the example learning method and Markov random field (MRF). Throughout the shadow renovation procedure, we model the connection between no shadow and also the corresponding shadow pixels and between neighboring no shadow pixels by using MRF. II. EXISTING SYSTEM Within this paper, a type of method with HSV is suggested to identify shadow in the color high res remote sensing imagery mainly through a number of processing steps including two times HSV transformation, self-adaptive segmentation, morphological closing operation and little area removing. Finally, the number of the Cisco kid is achieved based on the shadow area record analysis. Within this paper, a type of method with HSV is suggested to identify shadow in the color high res remote sensing imagery mainly through a number of processing steps including two times HSV transformation, self-adaptive segmentation, morphological closing operation and little area removing. The primary purpose of these studies was to look for the capacity of high spatial resolution satellite image data to discriminate plant life structural procedures in riparian and adjacent forested environments as defined while using Bc Terrestrial Ecosystem Mapping (TEM) plan. This paper presents data from the study carried out around town of Dunedin, Nz. A few of the existing projects are participating to identify the shaded region after which eliminate that region, however it has some drawbacks. The recognition from the edges is going to be affected mostly by the use of the exterior parameters. The advantage recognition process could be more useful within the recognition from the objects so the objects can be used as further processing. Within this process we've implement the location growing thresholding formula can be used to identify the Cisco kid region and extract the feature in the shadow region. Region growing is simplest in region-base image segmentation methods. The idea of region growing formula is examining the neighboring pixels from the initial seed points. Then see whether individuals neighboring pixels are put into the seed points or otherwise. We advise an automobile recognition frame operate in two phases like training and Recognition Phase. Within this video is split directly into frames. We conducting a pixel wise classification method one of the neighboring pixels within the parts of objects to acquiring the quantitative observations. Color transforms are put on separate vehicle and non-vehicle colors effectively. The Canny edge recognition method put on extract the neighborhood options that come with objects. Thus feature extraction process give exact leads to classification by thinking about the automobile colors and native features [3]. Further Construct the Dynamic Bayesian network (DBN) for vehicle classification. Imagery from all of these sensors is a vital supply of timely data you can use in a number of urban area applications. Presently, the extraction of road systems and building footprints from high-resolution imagery is completed by hand, which is both time intensive and costly. To relieve the shadow effects in high-resolution images for his or her further applications, this paper proposes a manuscript shadow recognition formula in line with the morphological filtering along with a novel shadow renovation formula in line with the example learning method. Within the shadow recognition stage, a preliminary shadow mask is generated through the thresholding method, after which, the noise and wrong shadow regions are removed through the morphological filtering method. The Cisco kid renovation stage includes two phases: the instance-based learning phase and also the inference phase. Throughout the example-based learning phase, the Cisco kid and also the corresponding no shadow pixels are first by hand sampled in the study scene, after which, these samples form a shadow library along with a no shadow library, that are correlated with a Markov random field (MRF). Throughout the inference phase, the actual Landover pixels are reconstructed in the corresponding shadow pixels by following a Bayesian belief propagation formula to resolve the MRF.  Fig.1.Framework of proposed system III. PROPOSED SYSTEM The entire procedure includes shadow recognition and shadow renovation stages by executing on the multispectral (MS) satellite picture of a major city scene. The primary contribution of the paper within the shadow recognition stage is the fact that we combine thresholding and morphological filtering techniques by thinking about the spectral characteristics of various land-cover types. The Cisco kid recognition stage includes three primary steps: thresholding, morphological filtering, and M. Manasa* et al.   (IJITR) INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND RESEARCH   Volume No.4, Issue No.6, October – November 2016, 4803-4806.  2320 –5547 @ 2013-2016 http://www.ijitr.com All rights Reserved.  Page | 4805 edge compensation [4]. First, an initial shadow mask comes through the thresholding method based on the spectral characteristics from the MS image. Then, this shadow mask is elaborated by morphological operations to filter noise and also the wrong shadow areas. Finally, the Cisco kid edges are compensated thinking about the results of penumbra and also the surrounding conditions of shadows on VHSR images. The Cisco kid renovation stage includes two primary steps: example-based training and shadow renovation via Bayesian belief propagation (BBP). Prior to the training step, the no shadow and shadow samples are first collected in the same image scene by hand by visual judgment. Then, working out samples formulates a no shadow library along with a shadow library that is correlated by an MRF. Using the trained no shadow and shadow libraries, the actual no shadow pixels could be reconstructed in the corresponding shadow pixels based on the derived shadow mask within the shadow recognition stage. Thresholding for Shadow Recognition: Because the NIR spectrum has greater reflectivity than visual spectrum for a lot of urban land-cover types, digital number (DN) values of urban VHSR images are greater within the NIR band compared to other artists. For shadow areas, the DN values within an NIR band stop by a greater degree due to the occlusion of sunlight. The research in [14] has proven the DN ratio of sunshine shadow and sunlight is gloomier in NIR band compared to RGB bands. Within this paper, we compare the ratios of no shadow and shadow pixel values in numerous bands of QB and WV-2 images, which is studied within our experimental part. Furthermore, the morphological operations may also take away the wrong shadow regions with appropriate prior information. Mathematical morphology is really a set- and lattice-theoretic methodology for image analysis, which aims to quantitatively describe the geometrical structure of image objects. Thus, morphological filters, for appropriate than straight line filters for shape analysis, play a significant role in geometry-based enhancement and recognition. The fundamental morphological operations include erosion and dilation. Having a structuring element, erosion shrinks the item, and dilation grows the item. When mixing erosion and dilation, two new operations are generated, namely, frequent lowering and rising, which keep your general form of objects but possess different smoothing effects. Particularly, the outlet removes small protrusions and thin connections, whereas the closing fills in small holes. Within this paper, we adopt frequent lowering and raising operations to get rid of the noise and wrong shadows in MT having a structuring component of 3 × 3 ones. Since the form of roads is generally thin, the outlet operation is used to MT once. Consequently, the street shadows are damaged into discrete small regions, which may be considered as noise in further steps. To get rid of the noise in no shadow areas, one opening operation by having an eight-connected neighborhood constraint and area specifications is used to MTO The fundamental concept of this process would be to exploit the connection of image and scene pairs within the training set to develop a learning model and also to make use of the learning model to derive a scene from the image [5]. This concept continues to be well put on image super resolution tasks because they build the connection from a high-resolution image and it is low-resolution version. Motivated with this application, we advise to construct the connection between shadow and no shadow pixels via this situation-based learning method. With light shadows within the VHSR image, we first extract shadow and no shadow pixel samples within the study scene for training purposes. IV. EXPERIMENTAL RESULTS In general, the reconstructed images are beneficial for the classification task because more underlying land covers in shadow regions can be classified correctly than in image before shadow reconstruction (all shadow regions are classified to one class).  (a)  (b)  (c) M. Manasa* et al.   (IJITR) INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND RESEARCH   Volume No.4, Issue No.6, October – November 2016, 4803-4806.  2320 –5547 @ 2013-2016 http://www.ijitr.com All rights Reserved.  Page | 4806  (d) Fig. 2.(a) Original image with shadows.  (b) Shadow mask after thresholding method.(c) Shadow mask after morphological filtering and edge compensation.         (d) Reconstructed image from our method. V. CONCLUSION To rebuild the actual scene pixels of shadows, we've created a shadow renovation formula in line with the example learning method as well as an MRF. For bigger scenes, the processing time should increase rapidly using the development of data amount. This paper has presented a brand new shadow recognition formula along with a new shadow renovation formula to cope with shadows on high-resolution satellite images. Mixing the Cisco kid qualities and spectral characteristics of objects, we've suggested making use of thresholding method and morphological filtering to identify shadows. It's worth mentioning that people considered the results of high-brightness areas around the adjoining shadow neighbors and integrated them in to the shadow mask by using morphological operations within the shadow recognition formula. Another matter important to note is the fact that we considered the compatibility between your reconstructed shadow regions as well as their no shadow surroundings and improved this problem by passing messages together in BBP procedure. The experimental results around the QB image and also the WV-2 image indicate the suggested shadow recognition formula can derive continuous and proper shadow masks which the reconstructed shadow regions in the suggested shadow renovation formula are in line with their surroundings. When compared with previous shadow renovation algorithms, the superiorities from the suggested one are that it doesn't require a classification step, it enables within class variations for just one land-cover type by only selecting individuals candidates which are most like the study pixel, also it keeps good compatibility between your reconstructed shadow regions as well as their surrounding no shadow regions. The classification test on images pre and post shadow renovation shows that the look after shadow renovation is advantageous to enhance the classification performance. VI. REFERENCES [1]  T. Kim, T. Javzandulam, and T.-Y. Lee, “Semiautomatic reconstruction of building height and footprints from single satellite images,” in Proc. IEEE IGARSS, Jul. 2007, vol. 2, pp. 4737–4741.  [2]  H. Ma, Q. Qin, and X. Shen, “Shadow segmentation and compensation in high resolution satellite images,” in Proc. IEEE IGARSS, Jul. 2008, vol. 2, pp. 1036–1039.  [3]  A. Katartzis and H. Sahli, “A stochastic framework for the identification of building rooftops using a single remote sensing image,” IEEE Trans. Geosci. Remote Sens., vol. 46, no. 1, pp. 259–271, Jan. 2008.  [4]  F. Yamazaki, W. Liu, and M. Takasaki, “Characteristics of shadow and removal of its effects for remote sensing imagery,” in Proc. IEEE IGARSS, Jul. 2009, vol. 4, pp. 426–429. [5]  W. T. Freeman, T. R. Jones, and E. C. Pasztor, “Example-based super resolution,” IEEE Comput. Graph. Appl., vol. 22, no. 2, pp. 56–65, Mar. /Apr. 2002 [6]  K. Johansen, N. C. Coops, S. E. Gergel, and Y. Stange, “Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification,” Remote Sens. Environ., vol. 110, no. 1, pp. 29–44, Sep. 2007. [7]  V. Tsai, “A comparative study on shadow compensation of color aerial images in invariant color models,” IEEE Trans. Geosci. Remote Sens., vol. 44, no. 6, pp. 1661–1671, Jun. 2006. [8]  S. Hinz and A. Baumgartner, “Vehicle detection in aerial images using generic features, grouping, and context,” in Proc. Pattern Recognit., Lect. Notes Comput. Sci., 2001, vol. 2191, pp. 45–52. [9]  M. Mokhtarzade and M. J. V. Zoej, “Road detection from high-resolution satellite images using artificial neural networks,” Int. J. Appl. Earth Observ.Geoinf., vol. 9, no. 1, pp. 32–40, Feb. 2007.  