Tri-level Unified Framework for Human Gait Analysis

There are several applications that can be related to multimedia content analysis. Considering video as one of the prominent forms of multimedia content, this paper presents analysis of human walking motion (gait) found in video sequences by using promising strategy of integrating techniques from data fusion and computer vision. To provide solutions to the challenges in human gait analysis a unified framework is proposed comprising of three different levels: data level, feature descriptor level and decision level. The three levels perform specific tasks assigned to them. At the data level, features are extracted from input video sequences for minimal representation. At the feature descriptor level, features from minimal representation are rearranged to build a feature descriptor and finally at decision level meaningful interpretations are performed. For analysing human walking motion found in video sequences, initially, moving silhouettes are extracted using background subtraction for minimal representation at the data level. The extracted silhouettes are then represented in a common representation in a spatial form followed by correlation analysis and a feature descriptor is developed with minimum interest points at the feature descriptor level. Finally, interpretation of normal gait poses and transition poses are made at the decision level.Keywords:Multimedia content; Data Fusion; Unified Framework; Background Subtraction;Correlation; Feature Descriptor; interpretation of Gaits

Retrieval of Images Using Color, Shape and Texture Features Based on Content

The current study deals with deriving of image feature descriptor by error diffusion based block truncation coding (EDBTC). The image feature descriptor is basically comprised by the two error diffusion block truncation coding, color quantizers and its equivalent bitmap image. The bitmap image distinguish the image edges and textural information of two color quantizers to signify the color allocation and image contrast derived by the Bit Pattern Feature and Color Co-occurrence Feature. Tentative outcome reveal the benefit of proposed feature descriptor as contrast to existing schemes in image retrieval assignment under normal and textural images. The Error-Diffusion Block Truncation Coding method compresses an image efficiently, and at the same time, its consequent compacted information flow can provides an efficient feature descriptor intended for operating image recovery and categorization. As a result, the proposed design preserves an effective candidate for real-time image retrieval applications

Compact and low-complexity binary feature descriptor and Fisher Vectors for video analytics

In this paper, we propose a compact and low- complexity binary feature descriptor for video analytics. Our binary descriptor encodes the motion information of a spatio- temporal support region into a low-dimensional binary string. The descriptor is based on a binning strategy and a construction that binarizes separately the horizontal and vertical motion components of the spatio-temporal support region. We pair our descriptor with a novel Fisher Vector (FV) scheme for binary data to project a set of binary features into a fixed length vector in order to evaluate the similarity between feature sets. We test the effectiveness of our binary feature descriptor with FVs for action recognition, which is one of the most challenging tasks in computer vision, as well as gait recognition and animal behavior clustering. Several experiments on the KTH, UCF50, UCF101, CASIA-B, and TIGdog datasets show that the proposed binary feature descriptor outperforms the state-of-the-art feature descriptors in terms of computational time and memory and stor- age requirements. When paired with FVs, the proposed feature descriptor attains a very competitive performance, outperforming several state-of-the-art feature descriptors and some methods based on convolutional neural networks

Brain structures identification based on feature descriptor

Traumatic Brain Injury -TBI- -1- is defined as an acute event that causes certain damage to areas of the brain. TBI may result in a significant impairment of an individuals physical, cognitive and psychosocial functioning. The main consequence of TBI is a dramatic change in the individuals daily life involving a profound disruption of the family, a loss of future income capacity and an increase of lifetime cost. One of the main challenges of TBI Neuroimaging is to develop robust automated image analysis methods to detect signatures of TBI, such as: hyper-intensity areas, changes in image contrast and in brain shape. The final goal of this research is to develop a method to identify the altered brain structures by automatically detecting landmarks on the image where signal changes and to provide comprehensive information to the clinician about them. These landmarks identify injured structures by co-registering the patient?s image with an atlas where landmarks have been previously detected. The research work has been initiated by identifying brain structures on healthy subjects to validate the proposed method. Later, this method will be used to identify modified structures on TBI imaging studies