Multi-Lingual Character Recognition and Extraction using Recurrent Neural Networks
A Novel Approach to Smart Autonomous Monitoring of Indoor Plant Health Based on Leaf Color
Edge Preserving Image Denoising with Deep Networks and Ensemble Convolutional Neural Network Architectures
Neural Network Approach to Detect Renal Calculi
Enhancing Fabric Quality Control: Implementing Real-Time Defect Detection with Image Processing Techniques and Arduino
Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images
Data Hiding in Encrypted Compressed Videos for Privacy Information Protection
Improved Video Watermarking using Discrete Cosine Transform
Contrast Enhancement based Brain Tumour MRI Image Segmentation and Detection with Low Power Consumption
Denoising of Images by Wavelets and Contourlets using Bi-Shrink Filter
This paper introduces bivariate thresholding based Dual Tree Complex Wavelet Transform (DTCWT) technique to remove both Gaussian and speckle noise signals. Since both types of noises are different in nature hence it is difficult to remove them by using single filter. In this paper DTCWT approach is used to denoise ultrasound images. DTCWT based filter removes Additive White Gaussian Noise (AWGN) effectively. Since speckle noise is multiplicative in nature; it is converted into logarithmic transform before applying wavelet transform. Bivariate shrinkage (soft thresholding) function is used.
During the past several decades, there were tremendous developments in the field of Image Segmentation. Due to the extreme thrust in enhancing the quality of the image segmentation process, numerous segmentation techniques have evolved. Segmentation in color images is quite difficult due to the uncertainties that exist at the boundary. Fuzzy logic is an ideal concept which is well suited in such cases. It is an approach of computation which is based on the degrees of truth rather than the Boolean logic through which the modern computer works. Fuzzy techniques are highly popular due to its rapid extension of fuzzy set theory and are mainly based on the binary valued membership. Thus the Fuzzy techniques applied in image processing can efficiently manage the ambiguities present in the images. Hence this comparative analysis mainly indicates the working methodologies of different collections of Fuzzy logic techniques in image segmentation and this in turn helps the image processing researchers to innovate more advanced techniques in Fuzzy concept and solve the problem in hand.
Image processing plays a vital role in all fields like satellite, medical, telecommunication, and missile. Hyper spectral images show similar statistical properties to natural grayscale or color photographic images. HSI (Hyper Spectral Image) are a more challenging area because of high spectral bands and dimensionality. It is also very easy to learn. It is used to identify the problems in various fields like Signal Processing, and moreover used to determine the complex manifolds. There are several algorithms which have been proposed to classify the hyper spectral image. In our paper new methods have been introduced, that is Harmonic Analysis based classification such as HA-PSO-RVM (Particle swarm Optimization – Relevance Vector Machine) approach. This new approach accurately classifies the cluster band with respect to their amplitude and phase. Harmonic Analysis (HA) is introduced to extract the features of hyper spectral image. Amplitude and phase features have been obtained by deriving HA. Then the best features are selected, among extracted features by Particle swarm Optimization. Finally, the respective bands are classified by a related cluster, which are performed by the help of Relevance Vector Machine (RVM). This classifier accurately classifies the band to respective cluster form. In prior work, instead of HA, the used MNF, PCA and ICA could extract features and also in combination of PSO-SVM could use CV-SVM and GA-SVM. Here, the process will be carried out by integrating HA-PSO-RVM. This combination leads to provide good accuracy and also limited computer time because of the usage of the PSO Method.
The rapid development of techniques requires fast and secure transmission of images through the network. Traditional way of secure transmission is, to encrypt the compressed images. Even if this meets security prerequisites, this situation may be reversed. Therefore, an efficient image encryption then compression system (ETC) in prediction error domain is introduced. In this system, image is encrypted by calculating the prediction error of each pixel followed by encoding techniques. The encoding is done by simple arithmetic coding method. Simulation results shows that bit rate can be reduced to 12.
The large size of video data makes it desirable to be stored and processed in the cloud. To provide security and privacy for the data in the cloud, the video data must be stored in encrypted form. It is also necessary to perform data hiding to avoid the leakage of video content. This paper proposes a novel method of embedding additional data in H.264/AVC video bit streams to meet the privacy preserving requirements of cloud data management. This method proposes three processes namely: H.264/AVC video encryption, Embedding of encrypted data and extraction of original data. Depending upon the property of H.264/AVC codec, the code words of I-frames and P-frames are encrypted with stream ciphers. The data to be embedded into the encrypted video is also encrypted by Chaos encryption technique and hidden into the video by bit replacement method. Data extraction is done both in encrypted domain and in decrypted domain. Experimental results show that the proposed method preserves file size, whereas degradation in video quality caused by data embedding is less.
In this paper, a new technique for image authentication is presented which uses semifragile watermarking scheme using vector quantization approach. The proposed scheme suggests semifragile watermarking scheme for blind image authentication using VQ (Vector quantization) by applying novel index based method combined with Discrete cosine transform. Present method is tested using, different combinations of code books for watermark insertion and extraction procedure . The proposed method results in an improved PSNR with an average value of 42 dB compared to average value 30 dB suggested by Lu et al.
