RECOGNITION OF CHARACTER FROM VIDEO SUBTITLES

An important task in content based video indexing is to extract text information from videos. The challenges involved in text extraction and recognition are variation of illumination on each video frame with text, the text present on the complex background and different font size of the text. Using various image processing algorithms like morphological operations, blob detection and histogram of oriented gradients the character recognition of video subtitles is implemented. Segmentation, feature extraction and classification are the major steps of character recognition. Several experimental results are shown to demonstrate the performance of the proposed algorithm

Automatic View-Point Selection for Inter-Operative Endoscopic Surveillance

International audienceAbstract. Esophageal adenocarcinoma arises from Barrett’s esophagus, which is the most serious complication of gastroesophageal reflux disease. Strategies for screening involve periodic surveillance and tissue biopsies. A major challenge in such regular examinations is to record and track the disease evolution and re-localization of biopsied sites to provide targeted treatments. In this paper, we extend our original inter-operativerelocalization framework to provide a constrained image based search for obtaining the best view-point match to the live view. Within this context we investigate the effect of, (a) the choice of feature descriptors and color-space, (b) filtering of uninformative frames, (c) endoscopic modality, for view-point localization. Our experiments indicate an improvement in the best view-point retrieval rate to [92%, 87%] from [73%, 76%] (in our previous approach) for NBI and WL

Recognition of compound characters in Kannada language

Recognition of degraded printed compound Kannada characters is a challenging research problem. It has been verified experimentally that noise removal is an essential preprocessing step. Proposed are two methods for degraded Kannada character recognition problem. Method 1 is conventionally used histogram of oriented gradients (HOG) feature extraction for character recognition problem. Extracted features are transformed and reduced using principal component analysis (PCA) and classification performed. Various classifiers are experimented with. Simple compound character classification is satisfactory (more than 98% accuracy) with this method. However, the method does not perform well on other two compound types. Method 2 is deep convolutional neural networks (CNN) model for classification. This outperforms HOG features and classification. The highest classification accuracy is found as 98.8% for simple compound character classification. The performance of deep CNN is far better for other two compound types. Deep CNN turns out to better for pooled character classes

Parking lot monitoring system using an autonomous quadrotor UAV

The main goal of this thesis is to develop a drone-based parking lot monitoring system using low-cost hardware and open-source software. Similar to wall-mounted surveillance cameras, a drone-based system can monitor parking lots without affecting the flow of traffic while also offering the mobility of patrol vehicles. The Parrot AR Drone 2.0 is the quadrotor drone used in this work due to its modularity and cost efficiency. Video and navigation data (including GPS) are communicated to a host computer using a Wi-Fi connection. The host computer analyzes navigation data using a custom flight control loop to determine control commands to be sent to the drone. A new license plate recognition pipeline is used to identify license plates of vehicles from video received from the drone

Shape Descriptors

Every day we recognize a numerous objects and human brain can recognize objects under many conditions. The way in which humans are able to identify an object is remarkably fast even in different size, colours or other factors. Computers or robots need computational tools to identify objects. Shape descriptors are one of the tools commonly used in image processing applications. Shape descriptors are regarded as mathematical functions employed for investigating image shape information. Various shape descriptors have been studied in the literature. The aim of this thesis is to develop new shape descriptors which provides a reasonable alternative to the existing methods or modified to improve them. Generally speaking shape descriptors can be categorized into various taxonomies based on the information they use to compute their measures. However, some descriptors may use a combination of boundary and interior points to compute their measures. A new shape descriptor, which uses both region and contour information, called centeredness measure has been defined. A new alternative ellipticity measure and sensitive family ellipticity measures are introduced. Lastly familiy of ellipticity measures, which can distinguish between ellipses whose ratio between the length of the major and minor axis differs, have been presented. These measures can be combined and applied in different image processing applications such as image retrieval and classification. This simple basis is demonstrated through several examples

Invariant encoding schemes for visual recognition

Many encoding schemes, such as the Scale Invariant Feature Transform (SIFT) and Histograms of Oriented Gradients (HOG), make use of templates of histograms to enable a loose encoding of the spatial position of basic features such as oriented gradients. Whilst such schemes have been successfully applied, the use of a template may limit the potential as it forces the histograms to conform to a rigid spatial arrangement. In this work we look at developing novel schemes making use of histograms, without the need for a template, which offer good levels of performance in visual recognition tasks. To do this, we look at the way the basic feature type changes across scale at individual locations. This gives rise to the notion of column features, which capture this change across scale by concatenating feature types at a given scale separation. As well as applying this idea to oriented gradients, we make wide use of Basic Image Features (BIFs) and oriented Basic Image Features (oBIFs) which encode local symmetry information. This resulted in a range of encoding schemes. We then tested these schemes on problems of current interest in three application areas. First, the recognition of characters taken from natural images, where our system outperformed existing methods. For the second area we selected a texture problem, involving the discrimination of quartz grains using surface texture, where the system achieved near perfect performance on the first task, and a level of performance comparable to an expert human on the second. In the third area, writer identification, the system achieved a perfect score and outperformed other methods when tested using the Arabic handwriting dataset as part of the ICDAR 2011 Competition