S. Padmavathi

Vehicle Detection in Static Images Using Color and Corner Map

This paper presents an approach to identify the vehicle in the static images using color and corner map. The detection of vehicles in a traffic scene can address wide range of traffic problems. Here an attempt has been made to reduce the search time to find the possible vehicle candidates thereby reducing the computation time without a full search. A color transformation is used to project all the colors of input pixels to a new feature space such that vehicle pixels can be easily distinguished from non-vehicle ones. Bayesian classifier is adopted for verifying the vehicle pixels from the background. Corner map is used for removing the false detections and to verify the vehicle candidates.

Conversion of Braille to Text in English, Hindi and Tamil Languages

.The Braille system has been used by the visually impaired for reading andwriting. Due to limited availability of the Braille text books an efficient usage of the books becomes a necessity. This paper proposes a method to convert a s canned Braille document to text which can be read out to many through the computer. The Braille documents are preprocessed to enhance the dots and reduce the noise. The Braille cells are segmented and the dots from each cell is extractedand converted in to a number sequence. These are mapped to the appropriate alphabets of the language.The converted text is spoken out through a speech synthesizer. The paper also provides a mechanism to type the Braille characters through the number pad of the keyboard. The typed Braille character is mappe d to the alphabet and spoken out.The Braille cell has a standard representation but the mapping differs for each language. In this paper mapping of English, Hindi and Tamil are considered.

Computer vision based approach for Indian Sign Language character recognition

Deaf and dumb people communicate among themselves using sign languages, but they find it difficult to expose themselves to the outside world. This paper proposes a method to convert the Indian Sign Language (ISL) hand gestures into appropriate text message. In this paper the hand gestures corresponding to ISL English alphabets are captured through a webcam. In the captured frames the hand is segmented and the state of fingers is used to recognize the alphabet. The features such as angle made between fingers, number of fingers that are fully opened, fully closed or semi closed and identification of each finger are used for recognition. Experimentation done for single hand alphabets and the results are summarised.

Hierarchical Approach for Total Variation Digital Image Inpainting

The art of recovering an image from damage in an undetectable form is known as inpainting.The manual work of inpainting is most often a very time consumingprocess. Due to digitalization of this technique, it is automatic and faster. In this paper, after the user selects the regions to be reconstructed, the algorithm automatically reconstruct the lost regions with the help of the information surrounding them.The existing methods perform very well when the region to be reconstructed is very small, but fails in proper reconstruction as the area increases. This paper describes a Hierarchical method by which the area to be inpainted is reduced in multiple levels and Total Variation(TV) method is used to inpaint in each level. This algorithm gives better performance when compared w ith other existing algorithms such as nearest neighbor interpolation, Inpainting through Blurring and Sobolev Inpainting.

A Hierarchical Search Space Refinement and Filling for Exemplar based Image Inpainting

There are many real world scenarios where a portion of the image is damaged or lost. Restoring such an image without prior knowledge or a reference image is a difficult task. Image inpainting is a method that focuses on reconstructing the damaged or missing portion of images based on the information available from undamaged areas of the same image. The existing methods fill the missing area from the boundary. Their performance varies while reconstructing structures and textures and many of them restrict the size of the area to be inpainted. In this paper exemplar based inpainting is adopted in a hierarchical framework. A hierarchical search space refinement and hierarchical filling are proposed in this paper which increases the accuracy and handles the extra cost due to multi resolution processing in a better way. The former tries to select an exemplar suitable at all resolution levels restricting the search space from the lower resolution level. The later fills the region at lower resolution level whose results are taken to the higher levels. This makes the non boundary pixels known in the higher resolution level which in turn helps in search space refinement while increasing accuracy.

A Survey of Different Stages for Monitoring Traffic Rule Violation

A traffic surveillance system is a controlled system that helps to monitor and regulate the traffic. In this paper, a method for extracting the license number of the vehicle that is exceeding the speed limit is proposed. A Study is conducted by covering various stages of monitoring system such as vehicle detection in the video, tracking the vehicle for speed calculation and extracting the vehicle number in the number plate that can be used in places with high public vicinity.

Texel identification using K-means clustering method

Identifying the smallest portion of the image that represents the entire image is a basic need for its efficient storage. Texture can be defined as a pattern that is repeated in a specific manner. The basic pattern that is repeated is called as Texel(Texture Element). This paper describes a method of extracting a Texel from the given textured image using K means clustering algorithm and validating it with the entire image. The number of gray levels in an image is reduced using a linear transformation function. The image is then divided in to sub windows of certain size. These sub windows are clustered together using K-means algorithm. Finally a heuristic algorithm is applied on the cluster labels to identify the Texel, which results in more than one candidate for Texel. The best among them is then chosen based on its similarity with the overall image. The similarity between the Texel and the image is calculated based on then Normalized Gray level co-occurrence matrix in the maximum gradient direction. Experiments are conducted on various texture images for various block sizes and the results are summarized.

Enhanced Defogging System on Foggy Digital Color Images

Images which are captured using camera can cause degradation in images by the effect of climatic conditions such as haze and fog. Image restoration makes a notable change in performing different application of computer vision and pattern recognition. The main aim of this paper is to improve the effect of fog and hazy images compared to the existing methods. The enhanced defogging system [EDS] consists of different image improvement techniques with a Dark Channel Prior [DCP] Algorithm to estimate the amount of fog is there in the images and transmission as well. Fusion based fog removal will reduce the amount of haze remained in those images. Experiments were done more than 100 images and the results are discussed below.

Image enhancement through pyramid histogram matching

Pyramids being an emerging technology in the field of image processing, this paper uses the same for enhancing images using histogram matching. It gives a detailed analysis of enhancing the images by improving their contrasts using histogram matching in the pyramid layers thus extracting the information from the images to the maximum possible. It also shows the variation in the contrast of the images when matched with different sets of images of different contrasts.

Feature ranking procedure for automatic feature extraction

Classifier allows the user to classify between different classes based on the features acquired. The goals and applications of different classifiers are different. As the feature selection is one of the important criteria. In this paper we introduce a method of ranking the features of one class with respect to another and it tells the user that in the training set which feature has higher ranking among the other. So this method tells which feature is insignificant in certain classes and it can be ruled out. The classification can be made so easily as for some cases, certain features creates confusion in the classifier and wrong interpretations are also occurs. In the training set, if a new data is given as input and this method able to tell the user that the features has a variation with respect to training data set and the feature ranking is calculated. This method automatically ranks the feature and feature selection can be made easier. So we can able to interpret from the significant and insignificant features.