A unified system for object detection, texture recognition, and context analysis based on the standard model feature set

Recently, a neuroscience inspired set of visual features was introduced. It was shown that this representation facilitates better performance than stateof-the-art vision systems for object recognition in cluttered and unsegmented images. In this paper, we investigate whether these features can be applied outside the scope of unsegmented object detection. We show that this outstanding performance extends to shape-based object detection in the usual windowing framework, to amorphous object detection as a texture classification task, and finally to context understanding These tasks are performed on a large set of images which were collected as a benchmark for the problem of scene understanding. The final system is able to reliably identify cars, pedestrians, bikes, sky, road, buildings and trees in a diverse set of images. 1

Study on Co-occurrence-based Image Feature Analysis and Texture Recognition Employing Diagonal-Crisscross Local Binary Pattern

In this thesis, we focus on several important fields on real-world image texture analysis and recognition. We survey various important features that are suitable for texture analysis. Apart from the issue of variety of features, different types of texture datasets are also discussed in-depth. There is no thorough work covering the important databases and analyzing them in various viewpoints. We persuasively categorize texture databases ? based on many references. In this survey, we put a categorization to split these texture datasets into few basic groups and later put related datasets. Next, we exhaustively analyze eleven second-order statistical features or cues based on co-occurrence matrices to understand image texture surface. These features are exploited to analyze properties of image texture. The features are also categorized based on their angular orientations and their applicability. Finally, we propose a method called diagonal-crisscross local binary pattern (DCLBP) for texture recognition. We also propose two other extensions of the local binary pattern. Compare to the local binary pattern and few other extensions, we achieve that our proposed method performs satisfactorily well in two very challenging benchmark datasets, called the KTH-TIPS (Textures under varying Illumination, Pose and Scale) database, and the USC-SIPI (University of Southern California ? Signal and Image Processing Institute) Rotations Texture dataset.九州工業大学博士学位論文 学位記番号:工博甲第354号　学位授与年月日:平成25年9月27日CHAPTER 1 INTRODUCTION|CHAPTER 2 FEATURES FOR TEXTURE ANALYSIS|CHAPTER 3 IN-DEPTH ANALYSIS OF TEXTURE DATABASES|CHAPTER 4 ANALYSIS OF FEATURES BASED ON CO-OCCURRENCE IMAGE MATRIX|CHAPTER 5 CATEGORIZATION OF FEATURES BASED ON CO-OCCURRENCE IMAGE MATRIX|CHAPTER 6 TEXTURE RECOGNITION BASED ON DIAGONAL-CRISSCROSS LOCAL BINARY PATTERN|CHAPTER 7 CONCLUSIONS AND FUTURE WORK九州工業大学平成25年

A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex

We describe a quantitative theory to account for the computations performed by the feedforward path of the ventral stream of visual cortex and the local circuits implementing them. We show that a model instantiating the theory is capable of performing recognition on datasets of complex images at the level of human observers in rapid categorization tasks. We also show that the theory is consistent with (and in some case has predicted) several properties of neurons in V1, V4, IT and PFC. The theory seems sufficiently comprehensive, detailed and satisfactory to represent an interesting challenge for physiologists and modelers: either disprove its basic features or propose alternative theories of equivalent scope. The theory suggests a number of open questions for visual physiology and psychophysics

Video content analysis for automated detection and tracking of humans in CCTV surveillance applications

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The problems of achieving high detection rate with low false alarm rate for human detection and tracking in video sequence, performance scalability, and improving response time are addressed in this thesis. The underlying causes are the effect of scene complexity, human-to-human interactions, scale changes, and scene background-human interactions. A two-stage processing solution, namely, human detection, and human tracking with two novel pattern classifiers is presented. Scale independent human detection is achieved by processing in the wavelet domain using square wavelet features. These features used to characterise human silhouettes at different scales are similar to rectangular features used in [Viola 2001]. At the detection stage two detectors are combined to improve detection rate. The first detector is based on shape-outline of humans extracted from the scene using a reduced complexity outline extraction algorithm. A Shape mismatch measure is used to differentiate between the human and the background class. The second detector uses rectangular features as primitives for silhouette description in the wavelet domain. The marginal distribution of features collocated at a particular position on a candidate human (a patch of the image) is used to describe statistically the silhouette. Two similarity measures are computed between a candidate human and the model histograms of human and non human classes. The similarity measure is used to discriminate between the human and the non human class. At the tracking stage, a tracker based on joint probabilistic data association filter (JPDAF) for data association, and motion correspondence is presented. Track clustering is used to reduce hypothesis enumeration complexity. Towards improving response time with increase in frame dimension, scene complexity, and number of channels; a scalable algorithmic architecture and operating accuracy prediction technique is presented. A scheduling strategy for improving the response time and throughput by parallel processing is also presented

Street Scenes : towards scene understanding in still images

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 171-182).This thesis describes an effort to construct a scene understanding system that is able to analyze the content of real images. While constructing the system we had to provide solutions to many of the fundamental questions that every student of object recognition deals with daily. These include the choice of data set, the choice of success measurement, the representation of the image content, the selection of inference engine, and the representation of the relations between objects. The main test-bed for our system is the CBCL StreetScenes data base. It is a carefully labeled set of images, much larger than any similar data set available at the time it was collected. Each image in this data set was labeled for 9 common classes such as cars, pedestrians, roads and trees. Our system represents each image using a set of features that are based on a model of the human visual system constructed in our lab. We demonstrate that this biologically motivated image representation, along with its extensions, constitutes an effective representation for object detection, facilitating unprecedented levels of detection accuracy. Similarly to biological vision systems, our system uses hierarchical representations.(cont.) We therefore explore the possible ways of combining information across the hierarchy into the final perception. Our system is trained using standard machine learning machinery, which was first applied to computer vision in earlier work of Prof. Poggio and others. We demonstrate how the same standard methods can be used to model relations between objects in images as well, capturing context information. The resulting system detects and localizes, using a unified set of tools and image representations, compact objects such as cars, amorphous objects such as trees and roads, and the relations between objects within the scene. The same representation also excels in identifying objects in clutter without scanning the image. Much of the work presented in the thesis was devoted to a rigorous comparison of our system to alternative object recognition systems. The results of these experiments support the effectiveness of simple feed-forward systems for the basic tasks involved in scene understanding. We make our results fully available to the public by publishing our code and data sets in hope that others may improve and extend our results.by Stanley Michael Bileschi.Ph.D

Learning a Dictionary of Shape-Components in Visual Cortex: Comparison with Neurons, Humans and Machines

PhD thesisIn this thesis, I describe a quantitative model that accounts for the circuits and computations of the feedforward path of the ventral stream of visual cortex. This model is consistent with a general theory of visual processing that extends the hierarchical model of (Hubel & Wiesel, 1959) from primary to extrastriate visual areas. It attempts to explain the first few hundred milliseconds of visual processing and Âimmediate recognitionÂ. One of the key elements in the approach is the learning of a generic dictionary of shape-components from V2 to IT, which provides an invariant representation to task-specific categorization circuits in higher brain areas. This vocabulary of shape-tuned units is learned in an unsupervised manner from natural images, and constitutes a large and redundant set of image features with different complexities and invariances. This theory significantly extends an earlier approach by (Riesenhuber & Poggio, 1999) and builds upon several existing neurobiological models and conceptual proposals.First, I present evidence to show that the model can duplicate the tuning properties of neurons in various brain areas (e.g., V1, V4 and IT). In particular, the model agrees with data from V4 about the response of neurons to combinations of simple two-bar stimuli (Reynolds et al, 1999) (within the receptive field of the S2 units) and some of the C2 units in the model show a tuning for boundary conformations which is consistent with recordings from V4 (Pasupathy & Connor, 2001). Second, I show that not only can the model duplicate the tuning properties of neurons in various brain areas when probed with artificial stimuli, but it can also handle the recognition of objects in the real-world, to the extent of competing with the best computer vision systems. Third, I describe a comparison between the performance of the model and the performance of human observers in a rapid animal vs. non-animal recognition task for which recognition is fast and cortical back-projections are likely to be inactive. Results indicate that the model predicts human performance extremely well when the delay between the stimulus and the mask is about 50 ms. This suggests that cortical back-projections may not play a significant role when the time interval is in this range, and the model may therefore provide a satisfactory description of the feedforward path.Taken together, the evidences suggest that we may have the skeleton of a successful theory of visual cortex. In addition, this may be the first time that a neurobiological model, faithful to the physiology and the anatomy of visual cortex, not only competes with some of the best computer vision systems thus providing a realistic alternative to engineered artificial vision systems, but also achieves performance close to that of humans in a categorization task involving complex natural images