Gait can be identified by observing static and dynamic parts of human body. In this paper a variant of gait energy image called change energy images (CEI) are generated to capture detailed static and dynamic information of human gait. Radon transform is applied to CEI in four different directions (vertical, horizontal and two opposite cross sections) considering four different angles to compute discriminative feature values. The extracted features are represented in the form of interval –valued type symbolic data. The proposed method is capable of recognizing an individual when he/she have variations in their gait due to different clothes they wear, in different normal conditions and carrying a bag. A similarity measure suitable for the proposed gait representation is explored for the purpose of establishing similarity match for gait recognition. Experiments are conducted on CASIA database B and the results have shown better recognition performance compared to some of the existing methods.

This paper reports the development of a vision system to automatically classify work-pieces with respect to their shape and color together with determining their location for manipulation by an in-house developed pick-and-place robot from its work-plane. The vision-based pick-and-place robot has been developed as part of a smart flexible manufacturing system for unloading work-pieces for drilling operations at a drilling workstation from an automatic guided vehicle designed to transport the work-pieces in the manufacturing work-cell. Work-pieces with three different shapes and five different colors are scattered on the work-plane of the robot and manipulated based on the shape and color specification by the user through a graphical user interface. The number of corners and the hue, saturation, and value of the colors are used for shape and color recognition respectively in this work. Due to the distinct nature of the feature vectors for the fifteen work-piece classes, all work-pieces were successfully classified using minimum distance classification during repeated experimentations with work-pieces scattered randomly on the work-plane.

In this paper a new type of information hiding skill in biomedical images is proposed through a combination of cryptography and digital watermarking to achieve the enhancement in confidential and authenticated data storage and secured transmission. Here patient's name and doctor's name are considered as patient's information which is encrypted using cryptography and embedded in the scan image of that patient through watermarking. RSA algorithm is used for encryption and higher order bit LSB replacement technique is used for embedding the information. The private keys are also embedded in the cover image to have better security and accurate recovery of the hidden information. The outcome of the proposed methodology shows that the hidden information doesn't affect the cover image and it can be recovered efficiently even from several noisy images. The strength of the proposed embedding scheme is also supported by several image quality matrices.

Advances in FPGA technology have dramatically increased the use of FPGAs for computer vision applications. The primary task for development of such FPGAs based systems is the interfacing of the analog camera with FPGA board. This paper describes the design and implementation of camera interface module required for connecting analog camera with Xilinx ML510 (Virtex–5 FXT) FPGA board having no video input port. Digilent VDEC1 video daughter card is used for digitizing the analog video into digital form. The necessary control logics for video acquisition and video display are designed using VHDL and Verilog, simulated in ModelSim, and synthesized using Xilinx ISE 12.1. Designed and implemented interfaces provide the real-time video acquisition and display.

In this paper image compression using hybrid wavelet transform is proposed. Hybrid wavelet transform matrix is formed using two component orthogonal transforms. One is base transform which contributes to global features of an image and another transform contributes to local features. Here base transform is varied to observe its effect on image quality at different compression ratios. Different transforms like Discrete Kekre Transform (DKT), Walsh, Real-DFT, Sine, Hartley and Slant transform are chosen as base transforms. They are combined with Discrete Cosine Transform (DCT) that contributes to local features of an image. Sizes of component orthogonal transforms are varied as 16-16, 32-8 and 64-4 to generate hybrid wavelet transform of size 256x256. Results of different combinations are compared and it has been observed that, DKT as a base transform combined with DCT gives better results for size 16x16 of both component transforms.

Removing the noise from digital color images plays a vital role in many of the image processing applications. Salt and Pepper noise is one type of the impulse noise which corrupts images during image capture or transmission or storage etc. This paper proposes and implements a new decision based median filter using cloud model to restore the highly corrupted digital color images. The proposed filter is tested on different images and shows better performance than standard median filter, adaptive median filter, decision based median filter and modified decision based median filter in terms of root mean square error, peak signal to noise ratio and image quality index.

Mammography is a special CT scan technique, which uses X-rays and high-resolution film to detect breast tumors efficiently. Mammography is used only in breast tumor detection, and images help physicians to detect diseases due to cells normal growth. Mammography is an effective imaging modality for early breast cancer abnormalities detection. Computer aided diagnosis helps the radiologists to detect abnormalities earlier than traditional procedures. In this paper, an automated mammogram classification method is presented. Symlet, singular value decomposition and weighted histograms are used for feature extraction in mammograms. The extracted features are classified using naïve bayes, random forest and neural network algorithms.

Vehicle Make and Model Recognition (VMMR) has emerged as a significant element of vision based systems because of its application in access control systems, traffic control and monitoring systems, security systems and surveillance systems, etc. So far a number of techniques have been developed for vehicle recognition. Each technique follows different methodology and classification approaches. The evaluation results highlight the recognition technique with highest accuracy level. In this paper we have pointed out the working of various vehicle make and model recognition techniques and compare these techniques on the basis of methodology, principles, classification approach, classifier and level of recognition After comparing these factors we concluded that Locally Normalized Harris Corner Strengths (LHNS) performs best as compared to other techniques. LHNS uses Bayes and K-NN classification approaches for vehicle classification. It extracts information from frontal view of vehicles for vehicle make and model recognition.