Patents by Inventor Balakrishna GUDLA
Balakrishna GUDLA has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11544348Abstract: Visual target tracking is task of locating a target in consecutive frame of a video. Conventional systems observe target behavior frames of the video. However, dealing with this problem is very challenging when video has illumination variations, occlusion, change in size and view of the object due to relative motion between camera and object. Embodiments of the present disclosure addresses this problem by implementing Neural Network (NN), its features and their corresponding gradients. Present disclosure explicitly guides the NN by feeding target object of interest (ToI) defined by a bounding box in the first frame of the video. With this guidance, NN generates target activation map via convolutional features map and their gradient maps, thus giving tentative location of the ToI to further exploit to locate target object precisely by using correlation filter(s) and peak location estimator, thus repeating process for every frame of video to track ToI accurately.Type: GrantFiled: March 4, 2019Date of Patent: January 3, 2023Assignee: TATA CONSULTANCY SERVICES LIMITEDInventors: Balakrishna Gudla, Krishna Rao Kakkirala, Srinivasa Rao Chalamala
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Patent number: 11270010Abstract: Biometric templates (template) are used to store biometric data of one or more users. As biometric data of each user is unique and non-replaceable, template security is a major concern. This disclosure relates generally to template protection, and more particularly to a method and system for biometric template protection. The system generates a final perturbation value for data in the template, based on a first binary code (BC), a second binary code (BC?), feature vectors corresponding to data in the template, and an initial random perturbation. The final perturbation and the feature vectors are mapped to the first binary code. The first binary code represents the template. Every time the first binary code is compromised, the value of first binary code is updated, and the whole process is repeated to generate corresponding final perturbation and then the mapping is done.Type: GrantFiled: September 16, 2019Date of Patent: March 8, 2022Assignee: Tata Consultancy Services LimitedInventors: Santosh Kumar Jami, Krishna Rao Kakkirala, Srinivasa Rao Chalamala, Ajeet Kumar Singh, Arun Kumar Jindal, Balakrishna Gudla, Bala Mallikarjunarao Garlapati
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Patent number: 10902543Abstract: Systems and methods for insertion of a watermark into images and tampering detection of the watermarked images by a Convolutional Neural Network (CNN) technique. The traditional systems and methods provide for detecting the tampering of the watermarked images by simply identifying a presence of an inserted watermark into an image but none them provide for inserting a random sequence into input image(s) and then detect the tampering by classifying the input image(s) by a neural network.Type: GrantFiled: March 14, 2019Date of Patent: January 26, 2021Assignee: Tata Consultancy Services LimitedInventors: Krishna Rao Kakkirala, Srinivasa Rao Chalamala, Bala Mallikarjunarao Garlapati, Balakrishna Gudla, Santosh Kumar Jami, Arun Kumar Jindal, Ajeet Kumar Singh
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Patent number: 10762662Abstract: Target tracking in a video is a highly challenging problem as the target may be effected by its appearance changes along the video, partial occlusions, background clutter, illumination variations, surrounding environment and also due to changes in the motion of the target. Embodiments of the present disclosure address this problem by implementing neural network for convolution feature maps and their gradient maps generation. The proposed two-class neural network (TCNN) is guided by feeding it target of interest defined by a bounding box in a first frame of the video. With this target guidance TCNN generates target activation map by using convolutional features and gradient maps. Target activation map gives tentative location of target, and this is further exploited to locate target precisely by using correlation filter(s) and peak location estimator based on identified context. This process repeats for every frame of the video to track the target accurately.Type: GrantFiled: March 12, 2019Date of Patent: September 1, 2020Assignee: Tata Consultancy Services LimitedInventors: Srinivasa Rao Chalamala, Balakrishna Gudla, Krishna Rao Kakkirala
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Publication number: 20200089899Abstract: Biometric templates (template) are used to store biometric data of one or more users. As biometric data of each user is unique and non-replaceable, template security is a major concern. This disclosure relates generally to template protection, and more particularly to a method and system for biometric template protection. The system generates a final perturbation value for data in the template, based on a first binary code (BC), a second binary code (BC?), feature vectors corresponding to data in the template, and an initial random perturbation. The final perturbation and the feature vectors are mapped to the first binary code. The first binary code represents the template. Every time the first binary code is compromised, the value of first binary code is updated, and the whole process is repeated to generate corresponding final perturbation and then the mapping is done.Type: ApplicationFiled: September 16, 2019Publication date: March 19, 2020Applicant: Tata Consultancy Services LimitedInventors: Santosh Kumar JAMI, Krishna Rao KAKKIRALA, Srinivasa Rao CHALAMALA, Ajeet Kumar SINGH, Arun Kumar JINDAL, Balakrishna GUDLA, Bala Mallikarjunarao GARLAPATI
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Publication number: 20200026987Abstract: Visual target tracking is task of locating a target in consecutive frame of a video. Conventional systems observe target behavior frames of the video. However, dealing with this problem is very challenging when video has illumination variations, occlusion, change in size and view of the object due to relative motion between camera and object. Embodiments of the present disclosure addresses this problem by implementing Neural Network (NN), its features and their corresponding gradients. Present disclosure explicitly guides the NN by feeding target object of interest (ToI) defined by a bounding box in the first frame of the video. With this guidance, NN generates target activation map via convolutional features map and their gradient maps, thus giving tentative location of the ToI to further exploit to locate target object precisely by using correlation filter(s) and peak location estimator, thus repeating process for every frame of video to track ToI accurately.Type: ApplicationFiled: March 4, 2019Publication date: January 23, 2020Applicant: Tata Consultancy Services LimitedInventors: Balakrishna GUDLA, Krishna Rao KAKKIRALA, Srinivasa Rao CHALAMALA
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Patent number: 10496894Abstract: System and method for text localization in images are disclosed. In an embodiment, a line and graphic eliminated image is received. Further, horizontal projection is performed on rows of the image to obtain a first flag vector, the flag vector indicates whether there is text in each row. Furthermore, a number of run-lengths of consecutive 1's and 0's is computed in the first flag vector. Moreover, text lines is extracted in the image based on the computed number of run-lengths of consecutive 1's and 0's in the first flag vector. Also, vertical projection is performed on the text lines to obtain a second flag vector for the text lines. Further, a number of run-lengths of consecutive 1's and 0's is computed in the second flag vectors. Furthermore, text is localized in the image based on the computed number of run-lengths of consecutive 1's and 0's in the second flag vectors.Type: GrantFiled: January 18, 2018Date of Patent: December 3, 2019Assignee: Tata Consultancy Services LimitedInventors: Santosh Kumar Jami, Srinivasa Rao Chalamala, Krishna Rao Kakkirala, Balakrishna Gudla, Arun Kumar Jindal, Bala Mallikarjunarao Garlapati, Sachin Premsukh Lodha, Ajeet Kumar Singh, Vijayanand Mahadeo Banahatti
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Publication number: 20190287264Abstract: Target tracking in a video is a highly challenging problem as the target may be effected by its appearance changes along the video, partial occlusions, background clutter, illumination variations, surrounding environment and also due to changes in the motion of the target. Embodiments of the present disclosure address this problem by implementing neural network for convolution feature maps and their gradient maps generation. The proposed two-class neural network (TCNN) is guided by feeding it target of interest defined by a bounding box in a first frame of the video. With this target guidance TCNN generates target activation map by using convolutional features and gradient maps. Target activation map gives tentative location of target, and this is further exploited to locate target precisely by using correlation filter(s) and peak location estimator based on identified context. This process repeats for every frame of the video to track the target accurately.Type: ApplicationFiled: March 12, 2019Publication date: September 19, 2019Applicant: Tata Consultancy Services LimitedInventors: Srinivasa Rao CHALAMALA, Balakrishna GUDLA, Krishna Rao KAKKIRALA
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Publication number: 20190287204Abstract: Systems and methods for insertion of a watermark into images and tampering detection of the watermarked images by a Convolutional Neural Network (CNN) technique. The traditional systems and methods provide for detecting the tampering of the watermarked images by simply identifying a presence of an inserted watermark into an image but none them provide for inserting a random sequence into input image(s) and then detect the tampering by classifying the input image(s) by a neural network.Type: ApplicationFiled: March 14, 2019Publication date: September 19, 2019Applicant: Tata Consultancy Services LimitedInventors: Krishna Rao KAKKIRALA, Srinivasa Rao CHALAMALA, Bala Mallikarjunarao GARLAPATI, Balakrishna GUDLA, Santosh Kumar JAMI, Arun Kumar JINDAL, Ajeet Kumar SINGH
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Publication number: 20180285675Abstract: System and method for text localization in images are disclosed. In an embodiment, a line and graphic eliminated image is received. Further, horizontal projection is performed on rows of the image to obtain a first flag vector, the flag vector indicates whether there is text in each row. Furthermore, a number of run-lengths of consecutive 1's and 0's is computed in the first flag vector. Moreover, text lines is extracted in the image based on the computed number of run-lengths of consecutive 1's and 0's in the first flag vector. Also, vertical projection is performed on the text lines to obtain a second flag vector for the text lines. Further, a number of run-lengths of consecutive 1's and 0's is computed in the second flag vectors. Furthermore, text is localized in the image based on the computed number of run-lengths of consecutive 1's and 0's in the second flag vectors.Type: ApplicationFiled: January 18, 2018Publication date: October 4, 2018Applicant: Tata Consultancy Services LimitedInventors: Santoshkumar JAMI, Srinivasa Rao CHALAMALA, Krishna Rao KAKKIRALA, Balakrishna GUDLA, Arun Kumar JINDAL, Bala Mallikarjunarao GARLAPATI, Sachin Premsukh LODHA, Ajeet Kumar SINGH, Vijayanand Mahadeo BANAHATTI