A Framework for Symmetric Part Detection in Cluttered Scenes

The role of symmetry in computer vision has waxed and waned in importance during the evolution of the field from its earliest days. At first figuring prominently in support of bottom-up indexing, it fell out of favor as shape gave way to appearance and recognition gave way to detection. With a strong prior in the form of a target object, the role of the weaker priors offered by perceptual grouping was greatly diminished. However, as the field returns to the problem of recognition from a large database, the bottom-up recovery of the parts that make up the objects in a cluttered scene is critical for their recognition. The medial axis community has long exploited the ubiquitous regularity of symmetry as a basis for the decomposition of a closed contour into medial parts. However, today's recognition systems are faced with cluttered scenes, and the assumption that a closed contour exists, i.e. that figure-ground segmentation has been solved, renders much of the medial axis community's work inapplicable. In this article, we review a computational framework, previously reported in Lee et al. (2013), Levinshtein et al. (2009, 2013), that bridges the representation power of the medial axis and the need to recover and group an object's parts in a cluttered scene. Our framework is rooted in the idea that a maximally inscribed disc, the building block of a medial axis, can be modeled as a compact superpixel in the image. We evaluate the method on images of cluttered scenes.Comment: 10 pages, 8 figure

Data mining based learning algorithms for semi-supervised object identification and tracking

Sensor exploitation (SE) is the crucial step in surveillance applications such as airport security and search and rescue operations. It allows localization and identification of movement in urban settings and can significantly boost knowledge gathering, interpretation and action. Data mining techniques offer the promise of precise and accurate knowledge acquisition techniques in high-dimensional data domains (and diminishing the “curse of dimensionality” prevalent in such datasets), coupled by algorithmic design in feature extraction, discriminative ranking, feature fusion and supervised learning (classification). Consequently, data mining techniques and algorithms can be used to refine and process captured data and to detect, recognize, classify, and track objects with predictable high degrees of specificity and sensitivity. Automatic object detection and tracking algorithms face several obstacles, such as large and incomplete datasets, ill-defined regions of interest (ROIs), variable scalability, lack of compactness, angular regions, partial occlusions, environmental variables, and unknown potential object classes, which work against their ability to achieve accurate real-time results. Methods must produce fast and accurate results by streamlining image processing, data compression and reduction, feature extraction, classification, and tracking algorithms. Data mining techniques can sufficiently address these challenges by implementing efficient and accurate dimensionality reduction with feature extraction to refine incomplete (ill-partitioning) data-space and addressing challenges related to object classification, intra-class variability, and inter-class dependencies. A series of methods have been developed to combat many of the challenges for the purpose of creating a sensor exploitation and tracking framework for real time image sensor inputs. The framework has been broken down into a series of sub-routines, which work in both series and parallel to accomplish tasks such as image pre-processing, data reduction, segmentation, object detection, tracking, and classification. These methods can be implemented either independently or together to form a synergistic solution to object detection and tracking. The main contributions to the SE field include novel feature extraction methods for highly discriminative object detection, classification, and tracking. Also, a new supervised classification scheme is presented for detecting objects in urban environments. This scheme incorporates both novel features and non-maximal suppression to reduce false alarms, which can be abundant in cluttered environments such as cities. Lastly, a performance evaluation of Graphical Processing Unit (GPU) implementations of the subtask algorithms is presented, which provides insight into speed-up gains throughout the SE framework to improve design for real time applications. The overall framework provides a comprehensive SE system, which can be tailored for integration into a layered sensing scheme to provide the war fighter with automated assistance and support. As more sensor technology and integration continues to advance, this SE framework can provide faster and more accurate decision support for both intelligence and civilian applications

Object detection and activity recognition in digital image and video libraries

This thesis is a comprehensive study of object-based image and video retrieval, specifically for car and human detection and activity recognition purposes. The thesis focuses on the problem of connecting low level features to high level semantics by developing relational object and activity presentations. With the rapid growth of multimedia information in forms of digital image and video libraries, there is an increasing need for intelligent database management tools. The traditional text based query systems based on manual annotation process are impractical for today\u27s large libraries requiring an efficient information retrieval system. For this purpose, a hierarchical information retrieval system is proposed where shape, color and motion characteristics of objects of interest are captured in compressed and uncompressed domains. The proposed retrieval method provides object detection and activity recognition at different resolution levels from low complexity to low false rates. The thesis first examines extraction of low level features from images and videos using intensity, color and motion of pixels and blocks. Local consistency based on these features and geometrical characteristics of the regions is used to group object parts. The problem of managing the segmentation process is solved by a new approach that uses object based knowledge in order to group the regions according to a global consistency. A new model-based segmentation algorithm is introduced that uses a feedback from relational representation of the object. The selected unary and binary attributes are further extended for application specific algorithms. Object detection is achieved by matching the relational graphs of objects with the reference model. The major advantages of the algorithm can be summarized as improving the object extraction by reducing the dependence on the low level segmentation process and combining the boundary and region properties. The thesis then addresses the problem of object detection and activity recognition in compressed domain in order to reduce computational complexity. New algorithms for object detection and activity recognition in JPEG images and MPEG videos are developed. It is shown that significant information can be obtained from the compressed domain in order to connect to high level semantics. Since our aim is to retrieve information from images and videos compressed using standard algorithms such as JPEG and MPEG, our approach differentiates from previous compressed domain object detection techniques where the compression algorithms are governed by characteristics of object of interest to be retrieved. An algorithm is developed using the principal component analysis of MPEG motion vectors to detect the human activities; namely, walking, running, and kicking. Object detection in JPEG compressed still images and MPEG I frames is achieved by using DC-DCT coefficients of the luminance and chrominance values in the graph based object detection algorithm. The thesis finally addresses the problem of object detection in lower resolution and monochrome images. Specifically, it is demonstrated that the structural information of human silhouettes can be captured from AC-DCT coefficients