In this paper, we propose two algorithms based on the popular Bit Plane Splicing Least Significant Bit (LSB) Technique for secret data hiding inside images. One major disadvantage of Bit Plane Splicing LSB technique is its low hiding capacity which results in severe degradation of the cover image upon hiding large amount of data. The proposed algorithms overcome this issue by imposing hiding rules based on the intensity level of pixels. In addition the method for data hiding is done in a non sequential manner using linear congruent random number generators. Experiment results show that the proposed techniques called Optimum Intensity Based Distributed Hiding (OIBDH) technique and Linear Congruent Optimum Intensity Based Distributed Hiding with Key (LC-OIBDH-k) outperforms Bit Plane Splicing LSB technique as they have better hiding capacity with less degradation in the cover image. Furthermore, the proposed algorithms are tested using absolute entropy curves and results show that our proposed techniques have lower absolute entropy difference compared to Bit Plane Splicing LSB technique in all the tested images and for different secret data sizes.

A human eye can detect a face in an image whether it is in a digital image or also in some video. The same thing is highly challenging for a machine. There are lots of algorithms available to detect human face. In this paper, a technique has been made to detect and locate the position of human faces in digital images. This approach has two steps. First, training the artificial neural network using Levenberg–Marquardt training algorithm and then the proposed algorithm has been used to detect and locate the position of the human faces from digital image. The proposed algorithm has been implemented for six color spaces which are RGB, YES, YUV, YCbCr, YIQ and CMY for each of the image formats bmp, jpeg, gif, tiff and png. For each color space training has been made for the image formats bmp, jpeg, gif, tiff and png. Finally, one color space and particular image format has been selected for face detection and location in digital image based on the performance and accuracy.

The paper not only proposed the latest Two-Dimensional Barcodes Image-processing Module, but also captured the smallest camera screens (320 240) with different focal distances and tried to find out “Finder Pattern” for positioning images. Further, use CROBU (Conversion Ratio of Basic Unit) the thesis proposed to convert 2-D barcodes into 1-pixel ratio to match images before judging recognition rate of 2-D barcodes through matching. Normally speaking, 2-D barcodes are deciphered and recognized by software while the thesis recognizes 2-D barcodes and enhances implementation speed up to 10-cm accurate max. using image matching. The 2-D barcodes image-processing module the thesis proposed does capture and standardize image with complicated background or raw edge, which enhances 2-D barcodes recognition rate. The main point of this study is to construct a platform to manage or suggest nutrients human body needs. The Quick Response Code image of 2-D barcodes represents vitamin and calories information. 2-D barcodes taken instantly by MATLAB and CCD camera can be used to list nutrients from foods you eat recently and suggest what else you should eat for the purpose of health management.

Presently considerable amount of work has been done in tele-monitoring which involves the transmission of bio-signals and medical images in the wireless media. Intelligent exchange of bio-signals amongst hospitals needs efficient and reliable transmission. Watermarking adds “ownership” information in multimedia contents to prove the authenticity, to verify signal integrity, or to achieve control over the copy process. This paper proposes a novel session based blind watermarking method with secret key by embedding binary watermark image into (Electrocardiogram) ECG signal. The ECG signal is a sensitive diagnostic tool that is used to detect various cardio-vascular diseases by measuring and recording the electrical activity of the heart in exquisite detail. The first part of this paper proposes a multi-resolution wavelet transform based system for detection ‘P’,‘Q’,‘R’,‘S’,‘T’ peaks complex from original ECG signal of human being. ‘R-R’ time lapse is an important component of the ECG signal that corresponds to the heartbeat of the concerned person. Abrupt increase in height of the ‘R’ wave or changes in the measurement of the ‘R-R’ interval denote various disorders of human heart. Similarly ‘P-P’, ‘Q-Q’, ‘S-S’, ‘T-T’ intervals also correspond to different disorders of heart and their peak amplitude envisages other cardiac diseases. In this proposed method the ‘P Q R S T’-peaks are marked and stored over the entire signal and the time interval between two consecutive ‘R’-peaks and other peaks interval are measured to detect anomalies in behavior of heart, if any. The peaks are achieved by the composition of Daubechies sub-bands wavelet of original ECG signal. The accuracy of the P, QRS and T components detection and interval measurement is achieved with high accuracy by processing and thresholding the original ECG signal. The second part of the paper proposes a Discrete Wavelet Transformation (DWT) and Spread Spectrum based watermarking technique. In this approach, the generated watermarked signal having an acceptable level of imperceptibility and distortion is compared to the Original ECG signal. Finally, a comparative study is done for the intervals of two consecutive ‘R-R’ peaks, ‘P-R’, ‘Q-T’, ‘QTc’, QRS duration, cardiac output between original P, QRS and T components detected ECG signal and the watermarked P,QRS and T components detected ECG signal.

Discrimination of protein coding regions called exons from noncoding regions called introns or junk DNA in eukaryotic cell is a computationally intensive task. But the dimension of the DNA string is huge; hence it requires large computation time. Further the DNA sequences are inherently random and have vast redundancy, hidden regularities, long repeats and complementary palindromes and therefore cannot be compressed efficiently. The objective of this study is to present an integrated signal processing algorithm that considerably reduces the computational load by compressing the DNA sequence effectively and aids the problem of searching for coding regions in DNA sequences. The presented algorithm is based on the Discrete Wavelet Transform (DWT), a very fast and effective method used for data compression and followed by comb filter for effective prediction of protein coding period-3 regions in DNA sequences. This algorithm is validated using standard dataset such as HMR195, Burset and Guigo and KEGG.

Classification and recognition of objects is interest of many researchers. Shape is a significant feature of objects and it plays a crucial role in image classification and recognition. The present paper assumes that the features that drastically affect the adulthood classification system are the Shape features (SF) of face. Based on this, the present paper proposes a new technique of adulthood classification by extracting feature parameters of face on Integrated Texton based LBP (IT-LBP) images. The present paper evaluates LBP features on facial images. On LBP Texton Images complex shape features are evaluated on facial images for a precise age classification.LBP is a local texture operator with low computational complexity and low sensitivity to changes in illumination. Textons are considered as texture shape primitives which are located with certain placement rules. The proposed shape features represent emergent patterns showing a common property all over the image. The experimental evidence on FGnet aging database clearly indicates the significance and accuracy of the proposed classification method over the other existing methods.

A fast filtering algorithm for color video based on Neighborhood Correlation Filtering is presented. By utilizing a 3 × 3 pixel template, the algorithm can discriminate and filter various patterns of noise spots or blocks. In contrast with many kinds of median filtering algorithm, which may cause image blurring, it has much higher edge preserving ability. Furthermore, this algorithm is able to synchronously reflect image quality via amount, location and density statistics. Filtering of detected pixels is done by NCF algorithm based on a noise adaptive mean absolute difference. The experiments show that the proposed method outperforms other state-of-the-art filters both visually and in terms of objective quality measures such as the mean absolute error (MAE), the peak-signal-to-noise ratio (PSNR) and the normalized color difference (NCD).