Morphological Residues And A General Framework For Image Filtering And Segmentation

Morphological residues represent an image in a hierarchical way by means of a decomposition of its structures and according to a size parameter λ. From this decomposition, we can obtain a relation between the different residual levels associated with the complexity of the image structures. In this work, we introduce a new method to filter out components of gray-scale images based on the morphological residue decomposition which takes into account a size parameter and a certain level of complexity of the different structures to be filtered. As we will illustrate, this complexity is associated with a set of new attributes of the image defined according to the information contained in its multi-resolution representation.20014219229Serra, J., (1982) Image Analysis and Mathematical Morphology, 1. , Academic PressSerra, J., (1988) Image Analysis and Mathematical Morphology: Theoretical Advances, 2. , Academic PressHeijmans, H.J.A.M., (1994) Morphological Image Operators, , Academic Press, Boston, MABreen, E.J., Jones, R., Attribute openings, thinnings, and granulometries (1996) Computer Vision and Image Processing, 64 (3), pp. 377-389Rosenfeld, A., Kak, A.C., (1982) Digital Picture Processing, 1. , Academic Press, 2nd editionGonzalez, R.C., Woods, R.E., (1993) Digital Image Processing, , Addison-WesleyVincent, L., Grayscale area opennings and closings, their efficient implementation and applications (1993) Mathematical Morphology and Its Applications to Signal Processing, pp. 22-27. , J. Serra and P. Salembier, Eds., UPC Publications, MayVachier, C., (1995) Extraction de caractéristiques, segmentation d'image et morphologie mathématique, , Ph.D. thesis, École Nationale des Mines de Paris, DecemberHaralick, R.M., Shapiro, L.G., Image segmentation techniques (1985) Computer, Vision, Graphics and Image Processing, 35, pp. 100-132Vincent, L., Soillet, P., Watersheds in digital spaces: An efficient algorithm based on immersion simmulations (1991) IEEE Trans. on Pattern Analysis and Machine Intelligence, 13 (6), pp. 583-598Beucher, S., Yu, X., Road recognition in complex traffic situations (1994) 7th IFAC/IFORS Simposium on Transportation Systems: Theory and Application of Advanced Technology, pp. 413-418. , Tianjin, China, AugustBeucher, S., Meyer, F., The morphological approach to segmentation: The watershed transformation (1993) Mathematical Morphology in Image Processing, 34, pp. 433-481. , Edward R. Dougherty, Ed., chapter 12, Marcel Dekker, New YorkGoutsias, J., Heijmans, H.J.A.M., (1997) Multiresolution signal decomposition schemes. Part 1: Linear and morphological pyramids, , Tech. Rep., Center of Imaging Science and Department of Electric and Computer EngineeringMatheron, G., (1975) Random Sets and Integral Geometry, , John Wiley, New YorkMatheron, G., (1967) Eléments pour une Théorie des Milieux Poreux, , MassonParis, ParisVincent, L., Fast opening functions and morphological granulometries (1994) SPIE Image Algebra and Morphological Image Processing V, 2300, pp. 253-267. , San Diego, CA, JulyTang, X., Vincent, L., Stewart, K., Automatic plankton image classification (1996) International Artificial Intelligence Review JournalDougherty, E., Pelz, J., Sand, F., Lent, A., Morphological image segmentation by local granulometric size distributions (1992) Journal of Electronic Imaging, 1 (1), pp. 46-60Regazzoni, C., Foresti, G., Venetsanopoulos, A., Statistical pattern spectrum for binary pattern recognition (1994) Mathematical Morphology and Its Applications to Image Processing, pp. 185-192. , Jean Serra and Pierre Soile, Eds., Computational Imaging and Vision, Kluwer Academic Publishers, The NetherlandsVincent, L., Local grayscale granulometries based on opening trees (1996) Mathematical Morphology and Its Applications to Image Signal and Processing, pp. 273-280. , Petro Maragos, Ronald W. Schafer, and Muhammad Akmal Butt, Eds., Computational Image and Vision, Kluwer Academic Publishers, The NetherlandsVincent, L., Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms (1993) IEEE Trans. Image Processing, 2 (2), pp. 176-201Serra, J., Salembier, P., Connected operators and pyramids (1993) Proceeding of SPIE Image Algebra and Mathematical Morphology, 93, pp. 164-175. , FebruarySalembier, P., Oliveiras, A., Garrido, L., Antiextensive connected operators for image and sequence processing (1998) IEEE Trans. Image Processing, 7 (4), pp. 555-57

Going Deeper into Action Recognition: A Survey

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin