The paper proposes an advanced convexity based segmentation algorithm for heavily camouflaged images. The convexity of the intensity function is used to detect camouflaged objects from complex environments. We take advantage of operator for the detection of 3D concave or convex graylevels to exhibit the effectiveness of camouflage breaking based on convexity. The biological motivation behind operator and its high robustness make it suitable for camouflage breaking. The traditional convexity based algorithm identifies the desired targets but in addition also identifies sub-targets due to their three dimensional behavior. The problem is overcome by combining the conventional algorithm with thresholding. The proposed method is able to eliminate the sub-targets leaving behind only the target of interest in the input image. The proposed method is compared with the conventional operator. It is also compared with some conventional edge based operator for performance evaluation.

Brain tumor, is one of the major causes for the increase in mortality among children and adults. Detecting the regions of brain is the major challenge in tumor detection. In the field of medical image processing, multi sensor images are widely being used as potential sources to detect brain tumor. In this paper, a wavelet based image fusion algorithm is applied on the Magnetic Resonance (MR) images and Computed Tomography (CT) images which are used as primary sources to extract the redundant and complementary information in order to enhance the tumor detection in the resultant fused image. The main features taken into account for detection of brain tumor are location of tumor and size of the tumor, which is further optimized through fusion of images using various wavelet transforms parameters. We discuss and enforce the principle of evaluating and comparing the performance of the algorithm applied to the images with respect to various wavelets type used for the wavelet analysis. The performance efficiency of the algorithm is evaluated on the basis of PSNR values. The obtained results are compared on the basis of PSNR with gradient vector field and big bang optimization. The algorithms are analyzed in terms of performance with respect to accuracy in estimation of tumor region and computational efficiency of the algorithms.

The important task of human tracking can be difficult to implement in real world environment as the videos can involve complex scenes, severe occlusion and even moving background. Tracking individual objects in a cluttered scene is an important aspect of surveillance. In addition, the systems should also avoid misclassification which can lead to inaccurate tracking. This paper makes use of an efficient image annotation for human tracking. According to the literature survey, this is the first paper which proposes the application of the image annotation algorithm towards human tracking. The method divides the video scene into multiple layers assigning each layer to the individual object of interest. Since each layer has been assigned to a specific object in the video sequence: (i) we can track and analyse the movement of each object individually (ii) The method is able to reframe from misclassification as each object has been assigned a respective layer. The error incurred by the system with movement from one frame to another is presented with detailed simulations and is compared with the conventional Horn–Schunck alone.

Image compression is the methodology of reducing the data space required to store an image or video. It finds great application in transferring videos and images over the web to reduce data transfer time and resource consumption. A number of methods based on DCT and DWT have been proposed in the past like JPEG, MPEG, EZW, SPIHT etc. The paper presents a review comparison between DCT and DWT compression techniques based on multiple important evaluation parameters like (i) mean squared error and SNR for different threshold values (ii) SNR values and mean squared error for different coefficients (iii) SNR values and mean squared error for different window size. In addition, the paper also makes two advanced studies (i) CPU utilization and compression ratio for different window sizes (ii) SNR and compression with different compression ratio. The experimentation is performed on multiple 8x8 jpeg images.

Tracking occluded objects at different depths has become as extremely important component of study for any video sequence having wide applications in object tracking, scene recognition, coding, editing the videos and mosaicking. This paper experiments with the capabilities of image annotation contour based tracking for occluded object. Image annotation is applied on 3 similar normal video sequences varying in depth. In the experiment, one bike occludes the other at a depth of 60 cm, 80 cm and 100 cm respectively. The effect on tracking is also analyzed with illumination variations using 3 different light sources in video sequences having objects occluding one another at same depth. The paper finally studies the ability of annotation to track the occluded object based on pyramids with variation in depth further establishing a threshold at which the ability of the system to track the occluded object fails. The contour of both the individual objects can’t be tracked due to the distortion caused by overlapping of the object pyramids. The thresholds established can be used as a bench mark to estimate the capability of different softwares. The paper further computes the frame by frame error incurred by the system, supported by detailed simulations. This system can be effectively used to achieve flawless tracking as the error in motion tracking can be corrected. This can be of great interest to computer scientists while designing surveillance systems etc.