On the use of edge orientation and distance for content-based image retrieval

[[abstract]]Recently, various features for content-based image retrieval (CBIR) have been proposed, such as texture, color, shape, and spatial features. In this paper we propose a new feature, called orientation-distance histogram for CBIR. Firstly, we transform the RGB color model of a given image to the HSI color model and detect edge points by using the H-vector information. Secondly, we evaluate the orientation-distance histogram from the edge points to form a feature vector. After normalization of feature, our proposed method can cope with most problems of variations in image. Finally, we show some results of query for real life images with the precision and recall rates to measure the performance. The experimental results show that the proposed retrieval method is efficient and effective[[notice]]補正完畢[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20051013~20051015[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Beijing, Chin

[[alternative]]An Efficient Shape-Representation Method for Content Based Image Retrieval

計畫編號：NSC93-2213-E032-006研究期間：200408~200507研究經費：593,000[[abstract]]以內容為基礎之影像查詢(CBIR)的研究可分為特徵選取、物件表示以及結果比 對。假如以物件的外形輪廓表示物件的特徵，那麼邊緣點偵測就是抽取這類特徵的第 一個步驟。當找完了邊緣點後，一個好的物件表示法必須能夠克服物件在影像中的移 位、旋轉、以及放大或縮小等問題。甚至對於物件外形在一定程度內的損毀下也必須 能夠有好的比對結果。這些問題都是在利用物件外形特徵來表示物件時以及比對過程 中相當重要的議題。 因此本計畫將提出一個有效率及強健的以物件外形特徵為基礎的影像查詢系統。 我們使用一快速的邊緣點偵測演算法來偵測出影像中所有可能的邊緣點，並提出一新 的物件表示法—爬山式序列表示法(Mountain Climbing Sequence (MCS))。此表示法 對於前面所提之影像中的移位、旋轉、以及放大或縮小等問題都可以達到不變的效果。 另外，由於邊緣點的偵測就目前的研究經驗上並無法保証能夠找一物件的完整外形， 因此我們也將嘗試在現有的外形特徵表示法下，克服物件外形不完整抽取的情況，甚 至於在物件少部份被遮蔽的狀況也能得到好的比對結果。[[sponsorship]]行政院國家科學委員

Mesure de similarité de graphes par noyau de sacs de chemins

La classification de graphes s'appuie généralement sur une mesure de similarité entre graphes utilisée ensuite par un classifieur. Nous proposons ici l'utilisation des méthodes à noyaux pour une application de reconnaissance de formes représentées à l'aide de graphes. Nous introduisons tout d'abord la notion de noyau de graphe que nous étendons en proposant des noyaux entre des sacs de chemins. Nos premiers résultats montrent l'intérêt de cette approche par rapport aux approches classiques de comparaison de graphes

Mobile based optical form evaluation system

Optical forms that contain multiple-choice answers are widely used both for electing students and evaluating student achievements in education systems in our country and worldwide. Optical forms are evaluated by employing optical mark recognition techniques through optical readers. High cost of these machines, limited access to them, long waiting time for evaluation results make the process hard for educationists working in cities or countries. In this study, a mobile application was developed for the educationists who own mobile phones or tablets for the purpose of evaluating students' answer sheets quickly and independent of location and optical readers. Optical form recognition, reading and evaluation processes are done on the image of student's answer sheet that is taken with the mobile phone or tablet of educationist. The Android based mobile application that we developed has a user-friendly interface, high success rate and is the first of our knowledge application that operates on mobile platforms in this field.</span

AUTOMATIC EXTRACTION OF RIVERS IN SATELLITE IMAGES USING GEOMETRIC ACTIVE CONTOURS

This work aims to define and test a method for the extraction of rivers in orbital images for regions that are seasonally flooded, ie, large areas containing more water bodies besides the river, such as Pantanal. In the proposed methodology, several tools from the area of Image Analysis and Computer Vision have been employed, performing a preprocessing, followed by a topological modeling that is built upon a skeletonization process followed by an analysis of this skeleton. Lastly, the methodology selects structure that represents the river, by performing a topological filtering. This process is responsible for the selection of points that initiate the process of delineation of rivers using a geometric active contour method, called .Level Set Method.. The methodology was evaluated qualitatively (visual) and quantitatively (numerical) using the criteria of completeness and correctness in a series of real images of the Pantanal region. The edges extracted from rivers, were projected onto the original images, thus allowing a qualitative assessment. With respect to the numerical results for the criteria of completeness and correctness, these were always above 80%, which shows that the methodology is very effective and robust for the community that needs to perform feature extraction in remote sensing image

Holistic and component plant phenotyping using temporal image sequence

Background: Image-based plant phenotyping facilitates the extraction of traits noninvasively by analyzing large number of plants in a relatively short period of time. It has the potential to compute advanced phenotypes by considering the whole plant as a single object (holistic phenotypes) or as individual components, i.e., leaves and the stem (component phenotypes), to investigate the biophysical characteristics of the plants. The emergence timing, total number of leaves present at any point of time and the growth of individual leaves during vegetative stage life cycle of the maize plants are significant phenotypic expressions that best contribute to assess the plant vigor. However, image-based automated solution to this novel problem is yet to be explored. Results: A set of new holistic and component phenotypes are introduced in this paper. To compute the component phenotypes, it is essential to detect the individual leaves and the stem. Thus, the paper introduces a novel method to reliably detect the leaves and the stem of the maize plants by analyzing 2-dimensional visible light image sequences captured from the side using a graph based approach. The total number of leaves are counted and the length of each leaf is measured for all images in the sequence to monitor leaf growth. To evaluate the performance of the proposed algorithm, we introduce University of Nebraska–Lincoln Component Plant Phenotyping Dataset (UNL-CPPD) and provide ground truth to facilitate new algorithm development and uniform comparison. The temporal variation of the component phenotypes regulated by genotypes and environment (i.e., greenhouse) are experimentally demonstrated for the maize plants on UNL-CPPD. Statistical models are applied to analyze the greenhouse environment impact and demonstrate the genetic regulation of the temporal variation of the holistic phenotypes on the public dataset called Panicoid Phenomap-1. Conclusion: The central contribution of the paper is a novel computer vision based algorithm for automated detection of individual leaves and the stem to compute new component phenotypes along with a public release of a benchmark dataset, i.e., UNL-CPPD. Detailed experimental analyses are performed to demonstrate the temporal variation of the holistic and component phenotypes in maize regulated by environment and genetic variation with a discussion on their significance in the context of plant science

Gesture Based Character Recognition

Gesture is rudimentary movements of a human body part, which depicting the important movement of an individual. It is high significance for designing efficient human-computer interface. An proposed method for Recognition of character(English alphabets) from gesture i.e gesture is performed by the utilization of a pointer having color tip (is red, green, or blue). The color tip is segment from back ground by converting RGB to HSI color model. Motion of color tip is identified by optical flow method. During formation of multiple gesture the unwanted lines are removed by optical flow method. The movement of tip is recoded by Motion History Image(MHI) method. After getting the complete gesture, then each character is extracted from hand written image by using the connected component and the features are extracted of the correspond character. The recognition is performed by minimum distance classifier method (Modified Hausdorf Distance). An audio format of each character is store in data-set so that during the classification, the corresponding audio of character will play

Gesture-based Numeral Extraction and Recognition Shree Prakash

In this work the extraction of numerals and recognition is done using gesture. Gestures are elementary movements of a human body part, and are the atomic components describing the meaningful motion of a person. It is of utmost importance in designing an intelligent and efficient human-computer interface. Two approaches are proposed for the extraction of numeral from gesture. In the first approach, numerals are formed using the finger gesture. The movement of the finger gesture is identified using optical flow method. A view-specific representation of movement is constructed, where movement is defined as motion over time. A temporal encoding is performed from different frames into a single frame. To achieve this we utilize motion history image (MHI) scheme which spans the time scale of gesture. In the second approach, gesture is performed by the use of a pointer like a pen whose tip is either red, green, or blue. In the scene multiple persons are present performing various activities, but our scheme only captures the gesture made by the desired object. HSI color model is used to segment the tip followed by the optical flow to segment the motion. After getting the temporal template, the features are extracted and the recognition is performed. Our second approach is invariant to uninteresting movements in the surrounding while capturing the gesture. Hence it will not affect the final result of recognition