Medical experts often examine hundreds of x-ray images searching for salient features that are used to detect pathological abnormalities. Inspired by our understanding of the human visual system, automated salient features detection methods have drawn much attention in the medical imaging research community. However, despite the efforts, detecting robust and stable salient features in medical images continues to constitute a challenging task. This is mainly attributed to the complexity of the anatomical structures of interest which usually undergo a wide range of rigid and non-rigid variations.
In this paper, we present a novel appearance-based salient feature extraction and matching method based on sparse Contourlet-based representation. The multi-scale and directional capabilities of the Contourlets is utilized to extract salient points that are robust to noise, rigid and non-rigid deformations. Moreover, we also include prior knowledge about local appearance of the salient points of the structure of interest. This allows for extraction of robust stable salient points that are most relevant to the anatomical structure of interest. 

Virtual Reality or Immersive Multimedia as it is sometimes known, is the realization of real-world environment in terms of video, audio and ambience like smell, airflow, background noise and various ingredients that make up the real world. This environment gives us a sense of reality as if we are living in a real world although the implementation of Virtual Reality is on a laboratory scale. Audio has attained unimaginable clarity by splitting the spectrum into various frequency bands appropriate for rendering on a number of speakers or acoustic wave-guides. The combination and synchronization of audio and video with better clarity has transformed the rendition matched in quality by 3D cinema. Virtual Reality still remains in research and experimental stages. The objective of this research is to explore and innovate the esoteric aspects of the Virtual Reality like stereo vision incorporating depth of scene, rendering of video on a spherical surface, implementing depth-based audio rendering, applying self-modifying wavelets to compress the audio and video payload beyond levels achieved hitherto so that maximum reduction in size of transmitted payload will be achieved. Considering the finer aspects of Virtual Reality we propose to implement like stereo rendering of video and multi-channel rendering of audio with associated back channel activities, the bandwidth requirements increase considerably. Against this backdrop, it becomes necessary to achieve more compression to achieve the real-time rendering of multimedia contents effortlessly.

Gesture based communications are utilized as a primary method of correspondence, however, the differing qualities in the sign image portrayal limit its use to district bound. There is a tremendous assorted quality in the sign image portrayal from one nation to another, one state to another. In India, there is distinctive gesture-based communication watched for each state locale. It is henceforth exceptionally troublesome for one area individual to convey to other utilizing a signature image. This paper proposes a curvilinear tracing approach for the shape portrayal of Kannada communication via gestures acknowledgment. To build up this approach, a dataset is consequently made with all Swaragalu, Vyanjanagalu, Materials and Numbers in Kannada dialect. The arrangement of the dataset is framed by characterizing a vocabulary dataset for various sign images utilized as a part of regular interfacing. In the portrayal of gesture-based communication for acknowledgment, edge elements of hand areas are thought to be an ideal element portrayal of communication through signing. In the preparing of gesture-based communication, the agent includes assumes a critical part in arrangement execution. For the developed approach of sign language detection, where a single significant transformation is carried out, a word level detection is then performed. To represent the processing efficiency, a set of cue symbols is used for formulating a word. This word symbols are then processed to evaluate the performance for sign language detection. Word processing is carried out as a recursive process of a single cue symbol representation, where each frame data are processed for a curvilinear shape feature. The frame data are extracted based on the frame reading rate and multiple frames are processed in successive format to extract the region of interest. A system outline to process the video data and to give an optimal frame processing for sign recognition a word level process is performed.

Lack of apparent shape and texture features in disease recognition (Powdery Mildew and Anthracnose) of crop is a key challenge of Agriculture domain in the last few decades. The various soft computing techniques exists in computer vision system still there is need of most efficient methods to meet accuracy. In this work An enhanced Wavelet-PCA based Statistical Feature Extraction technique along with Modified Rotation Kernel Transformation (MRKT) based directional features is proposed in order to address the issues arising in different methodologies for plant disease recognition. This enhanced scheme extracts twenty wavelet features in addition to twelve direction features for different plant parts mango flower, fruit and leaf. This research work is an extended part presents in reference 1 by the authors. The feature set of total 32 features is used to train with Artificial Neural Network to diagnose both Powdery Mildew and Anthracnose disease which occur in the form of Fungus and black spots respectively on different parts of mango plant. The results obtained are found with accuracy of 98.50%, 98.75%, and 98.70% respectively for flower, fruit and leaf 

Rapid growth of internet service attains better security of multimedia contents now a days. Heading this problem a DCT-based color image watermarking framework is proposed in this article. Many earlier works have suggested embedding watermark information in the low frequencies of the image to enhance the robustness against JPEG compression because low frequencies hold the most significant information of the image and not affected significantly by the quantization method of JPEG algorithm. Replacement of low-frequency components with watermark directly may incur undesirable degradation to the image quality. To preserve the visual quality of watermarked images, we are proposing a watermarking framework that adjusts the DCT low-frequency coefficients by scaled averaging. The security issue is well-taken care with double secret keys. Experimental result set demonstrates that the embedded watermark can be extracted efficiently from the JPEG-compressed images even after very high compression, re-watermarking, other image processing attacks. The extraction algorithm is blind i.e., neither host image nor the watermark is needed at the time of extraction.

Image steganography have been observed as one of the useful techniques to prevent unintended users to understand what information has been communicated over the network. Many crucial methods of image steganography include Discrete Cosine Transformation (DCT) based approaches. In this paper an efficient algorithm in image steganography is proposed extending DCT based approaches and incorporating number theory. The existing DCT algorithm is modified to increase randomness in the embedding technique with the help of Lucas sequence specifically. The effectiveness of the proposed method has been evaluated by computing Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). Results show that the proposed method has higher embedding capacity and increases a significant level of security by using Lucas sequence in addition to the advantages provided by existing DCT algorithm.

Texture classification and analysis are the most significant research topics in computer vision. Local binary pattern (LBP) derives distinctive features of textures. The robustness of LBP against gray-scale and monotonic variations and computational advantage have made it popular in various texture analysis applications. The histogram techniques based on LBP is complex task. Later uniform local binary pattern’s (ULBP) are derived on LBP based on bit wise transitions. The ULBP’s are rotationally invariant. The ULBP approach treated all non-uniform local binary pattern’s (NULBP) into one miscellaneous label. This paper presents a new texture classification method incorporating the properties of ULBP and grey-level co-occurrence matrix (GLCM). This paper derives ternary patterns on the ULBP and divides the 3 x 3 neighborhood in to dual neighborhood. The ternary pattern mitigates the noise problems particularly near uniform regions. The dual neighborhood reduces the range of texture unit from 0 to 6561 to 0 to 80. The GLCM features extracted from ULBP-dual texture matrix (ULBP-DTM) provide complete texture information about the image and reduce the texture unit range. Various machine learning classifiers are used for classification purpose. The performance of the proposed method is tested on Brodtaz, Outex and UIUC’s textures and compared with GLCM, texture spectrum (TS) and cross-diagonal texture matrix (CDTM) approaches.