Dr. Nagachetan Bangalore M Profile

Dr. Nagachetan Bangalore M

Chief Executive Officer at Visio Ingenii Ltd

SPIE Involvement:

Author

Area of Expertise:

Machine vision , Neural networks , Augmented reality , Robotics , Optical engineering , Artificial intelligence

Websites:

Company Website

Publications (6)

Proceedings Article | 19 February 2013 Paper

Determinant of homography-matrix-based multiple-object recognition

Nagachetan Bangalore, Madhu Kiran, Anil Suryaprakash

Proceedings Volume 8656, 86560M (2013) https://doi.org/10.1117/12.2003767

KEYWORDS: Object recognition, Databases, Tolerancing, Image compression, Machine vision, Computer vision technology, Light sources and illumination, Data modeling, Detection and tracking algorithms, Visualization

Read Abstract +

Finding a given object in an image or a sequence of frames is one of the fundamental computer vision challenges. Humans can recognize a multitude of objects with little effort despite scale, lighting and perspective changes. A robust computer vision based object recognition system is achievable only if a considerable tolerance to change in scale, rotation and light is achieved. Partial occlusion tolerance is also of paramount importance in order to achieve robust object recognition in real-time applications. In this paper, we propose an effective method for recognizing a given object from a class of trained objects in the presence of partial occlusions and considerable variance in scale, rotation and lighting conditions. The proposed method can also identify the absence of a given object from the class of trained objects. Unlike the conventional methods for object recognition based on the key feature matches between the training image and a test image, the proposed algorithm utilizes a statistical measure from the homography transform based resultant matrix to determine an object match. The magnitude of determinant of the homography matrix obtained by the homography transform between the test image and the set of training images is used as a criterion to recognize the object contained in the test image. The magnitude of the determinant of homography matrix is found to be very near to zero (i.e. less than 0.005) and ranges between 0.05 and 1, for the out-of-class object and in-class objects respectively. Hence, an out-of-class object can also be identified by using low threshold criteria on the magnitude of the determinant obtained. The proposed method has been extensively tested on a huge database of objects containing about 100 similar and difficult objects to give positive results for both out-of-class and in-class object recognition scenarios. The overall system performance has been documented to be about 95% accurate for a varied range of testing scenarios.

Proceedings Article | 23 April 2012 Paper

Improving OT-MACH filter performance for target recognition applications with the use of a Rayleigh distribution filter

Ahmad Alkandri, Nagachetan Bangalore, Akber Gardezi, Philip Birch, Rupert Young, Chris Chatwin

Proceedings Volume 8398, 83980D (2012) https://doi.org/10.1117/12.918756

KEYWORDS: Image filtering, Linear filtering, Forward looking infrared, Gaussian filters, 3D modeling, Target recognition, Optical filters, Computer aided design, Target detection, Wavelets

Read Abstract +

Proceedings Article | 28 September 2011 Paper

Automatic parameter adjustment of difference of Gaussian (DoG) filter to improve OT-MACH filter performance for target recognition applications

Ahmad Alkandri, Akber Gardezi, Nagachetan Bangalore, Philip Birch, Rupert Young, Chris Chatwin

Proceedings Volume 8185, 81850M (2011) https://doi.org/10.1117/12.897309

KEYWORDS: Optical filters, Gaussian filters, 3D modeling, Target detection, Image filtering, Computer aided design, Forward looking infrared, Target recognition, Bandpass filters, 3D acquisition

Read Abstract +

Proceedings Article | 3 June 2011 Paper

Door surveillance using edge map-based Harris corner detector and active contour orientation

Nagachetan Bangalore, Waqas Hassan, Bhargav Mitra, Philip Birch, Rupert Young, Chris Chatwin

Proceedings Volume 8056, 805608 (2011) https://doi.org/10.1117/12.883843

KEYWORDS: Surveillance, Cameras, Sensors, Corner detection, Hough transforms, Video, Video surveillance, Detection and tracking algorithms, Surveillance systems, Image filtering

Read Abstract +

Accurately generating an alarm for a moving door is a precondition for tracking, recognizing and segmenting objects or people entering or exiting the door. The challenge of generating an alarm when a door event occurs is difficult when dealing with complex doors, moving cameras, objects moving or an obscured entrance of the door, together with the presence of varying illumination conditions such as a door-way light being switched on. In this paper, we propose an effective method of tracking the door motion using edge-map information contained within a localised region at the top of the door. The region is located where the top edge of the door displaces every time the door is opened or closed. The proposed algorithm uses the edge-map information to detect the moving corner in the small windowed area with the help of a Harris corner detector. The moving corner detected in the selected region gives an exact coordinate of the door corner in motion, thus helping in generating an alarm to signify that the door is being opened or closed. Additionally, due to the prior selection of the small region, the proposed method nullifies the adverse effects mentioned above and helps prevent different objects that move in front of the door affecting its efficient tracking. The proposed overall method also generates an alarm to signify whether the door was displaced to provide entry or exit. To do this, an active contour orientation is computed to estimate the direction of motion of objects in the door area when an event occurs. This information is used to distinguish between objects and entities entering or exiting the door. A Hough transform is applied on a specific region in the frame to detect a line, which is used to perform error correction to the selected windows. The detected line coordinates are used to nullify the effects of a moving camera platform, thus improving the robustness of the results. The developed algorithm has been tested on all the Door Zone video sequences contained with the United Kingdom Home Office i-LIDs dataset, with promising results.

Proceedings Article | 26 April 2011 Paper

Robust human intrusion detection technique using hue-saturation histograms

Waqas Hassan, Bhargav Mitra, Nagachetan Bangalore, Philip Birch, Rupert Young, Chris Chatwin

Proceedings Volume 8055, 80550J (2011) https://doi.org/10.1117/12.883973

KEYWORDS: Video, Distortion, RGB color model, Image segmentation, Light sources and illumination, Computer intrusion detection, Cameras, Chromium, Binary data, Detection and tracking algorithms

Read Abstract +

Proceedings Article | 26 April 2011 Paper

Tracking illegally parked vehicles using correlation of multi-scale difference of Gaussian filtered patches

Bhargav Mitra, Waqas Hassan, Nagachetan Bangalore, Philip Birch, Rupert Young, Chris Chatwin

Proceedings Volume 8055, 805503 (2011) https://doi.org/10.1117/12.883821

KEYWORDS: Image filtering, Distortion, Bandpass filters, Electronic filtering, Video, RGB color model, Gaussian filters, Image segmentation, Video surveillance, Algorithm development

Read Abstract +

Showing 5 of 6 publications

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years