Mahesh Kumar Gellaboina

Graphic Symbol Recognition Using Auto Associative Neural Network Model

Symbol recognition is a well-known problem in the field of graphics. A symbol can be defined as a structure within document that has a particular meaning in the context of the application. Due to their representational power, graph structures are usually used to represent line drawings images.An accurate vectorization constitutes a first approach to solve this goal. But vectorization only gives the segments constituting the document and their geometrical attributes.Interpreting a document such as P&ID (Process & Instrumentation)diagram requires an additional stage viz. recognition of symbols in terms of its shape. Usually a P&ID diagram contain several types of elements, symbols and structural connectivity. For those symbols that can be defined by a prototype pattern, we propose an iterative learning strategy based on Hopfield model to learn the symbols, for subsequent recognition in the P&ID diagram. In a typical shape recognition problem one has to account for transformation invariance. Here the transformation invariance is circumvented by using an iterative learning approach which can learn symbols with high degree of correlation.

A semi-automatic relative calibration of a fixed and PTZ camera pair for master-slave control

A PTZ camera has the capability to cater to a much larger area of coverage when compared to a fixed field of view (FOV) camera. However, to utilize the full capability of a PTZ camera, it is necessary to have analytics to support event based autonomous camera control, such as automatic tracking of moving objects (people and vehicles), and zooming on to a face or car license plate to get a closer look. This is achieved by operating a PTZ camera in tandem with a fixed camera or a single PTZ camera operating stand alone under a master-slave control mode. One critical requirement of master-slave control operation is establishing a relative calibration between the master camera view and PTZ camera orientation parameters (pan, tilt and zoom angles). This paper presents a semiautomatic approach1 for solving relative calibration between the master and slave camera pairs that can be used in field with minimum effort.

Enhancement of low light image based on Gaussian Mixture Modeling

This paper presents a method for global image enhancement based on Gaussian Mixture Modeling (GMM)1. The philosophy behind GMM based enhancement is to enable a more efficient packing of individual Gaussian components in the histogram of the enhanced image, which gets rid off unutilized brightness zones in the image. This enhancement technique falls under the category of global image enhancement and is applied to the image using a transfer function. This method has been tested on diverse set of images under low light conditions.

Analog dial gauge reader for handheld devices

In a typical process industry, there are several critical dial gauges wherein the readings are to be monitored on a periodic basis. Most of these dial gauges are still analog in nature and currently these are being monitored manually. The manual capture of readings is a tedious task and is also prone to error. This paper outlines an algorithm that can automatically identify the dial gauge readings using image captured by a PDA device. This will help in reducing the error in readings as well to provide a record of the actual reading for future reference. Our algorithm uses polar representation of the dial gauge image to identify the needle position as well as the start position of the dial gauge. Once these are obtained the reading of the dial gauge can be estimated with prior calibration information. The algorithm was tested on multiple dial gauge images captured in a local chiller plant and is shown to obtain the readings reliably in about 95% of these images.

Flare monitoring for petroleum refineries

In petroleum refineries, it is a common practice to flare up the exhaust gases before releasing them to the atmosphere in order to reduce the environment pollution. The area or volume of the flare indicates the quantity of gas that is getting released in the refining process and the color of the flare at any given time is decided by the constituents of the gases in the flare and the volume of the flare indicates the quantity of gas that is released. In an indirect way, these parameters of flare indicate the performance of refining process. Presently, the flare is manually observed by the operator and doing so reliably on a 24×7 basis is a difficult job. In this paper we propose an algorithm1 to automate this effort using video analytics and provide the alarms in case of any abnormal flaring event. The flare detection is done by estimating models for foreground and background regions and the color of the flare is analyzed by using classifiers with kernel function, such as support vector machine (SVM). The performance of these algorithms is tested on various data sets collected from refineries.

Aircraft push back direction indicator

One of the common ramp activities in an airport is the push-back operation. It involves an aircraft being pushed back off the airport terminal gates (in the apron area) and aligning it towards a taxiway leading to the designated runway prior to departure. The aircraft is assigned a designated taxiway depending on traffic conditions and once the aircraft is aligned with the corresponding taxiway, the taxiway is activated for taxing using ground lighting. There is a need to automatically identify the direction of the aircraft alignment after pushback so as to ensure correct alignment and also to activate the corresponding taxiway. In this paper, a novel real-time solution is proposed for automatically detecting the aircraft pushback direction time using feeds from existing video cameras installed at the gates. The algorithm achieves this objective by obtaining the most stable and significant optical flows from the scene sequence using motion-based segmentation and simultaneous calculation of their orientation direction. Algorithm is designed to perform well during day/nightconditions, in deteriorating climatic conditions with very poor visibility and at low resolution (160×120) and low fps (less than 5 fps).