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公开(公告)号：US10210251B2
公开(公告)日：2019-02-19
申请号：US14188979
申请日：2014-02-25
Applicant: Tata Consultancy Services Limited
Inventor： Shailesh Shankar Deshpande , Girish Keshav Palshikar , Athiappan G
IPC: G06F17/30
Abstract: Disclosed is a method and system for creating labels for cluster in computing environment. The system comprises receiving module, candidate items selector, combination array generator, coverage value analyzer, candidate pair selector, unique word filter and cluster label selector. Receiving module receives input data and candidate items selector selects candidate items occurring repetitively using n-gram technique to generate list of candidate items with frequency of occurrence. Combination array generator selects candidate items to populate two-dimensional array wherein each array element represents pair of n-gram. Coverage value analyzer determines coverage value for each pair of n-gram from array. Candidate pair selector selects pairs of n-gram from two-dimensional array to process and generate list of candidate pairs. The unique word filter determines number of unique words in each candidate pair. Cluster label selector sorts list of candidate pairs using coverage value and number of unique words to select cluster label.
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公开(公告)号：US11978236B2
公开(公告)日：2024-05-07
申请号：US17513011
申请日：2021-10-28
Applicant: Tata Consultancy Services Limited
IPC: G06V10/24 , G06T7/11 , G06V10/25 , G06V10/44 , G06V10/774
CPC classification number: G06V10/245 , G06T7/11 , G06V10/25 , G06V10/44 , G06V10/7747 , G06T2207/10032 , G06T2207/20168
Abstract: State of art techniques performing image labeling of remotely sensed data are computation intensive, consume time and resources. A method and system for efficient retrieval of a target in an image in a collection of remotely sensed data is disclosed. Image scanning is performed efficiently, wherein only a small percentage of pixels from the entire image are scanned to identify the target. One or more samples are intelligently identified based on sample selection criteria and are scanned for detecting presence of the target based on cumulative evidence score Plurality of sampling approaches comprising active sampling, distributed sampling and hybrid sampling are disclosed that either detect and localize the target or perform image labeling indicating only presence of the target.
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公开(公告)号：US11615603B2
公开(公告)日：2023-03-28
申请号：US17201710
申请日：2021-03-15
Applicant: Tata Consultancy Services Limited
IPC: G06V10/00 , G06V10/143 , G06V10/25 , G06V10/40 , G06F18/2413 , G06F18/2431 , G06N3/045 , G06V10/58
Abstract: The embodiments herein provide a method and system that analyzes the pixel vectors by transforming the pixel vector into two-dimensional spectral shape space and then perform convolution over the image of graph thus formed. Method and system disclosed converts the pixel vector into image and provides a DCNN architecture that is built for processing 2D visual representation of the pixel vectors to learn spectral and classify the pixels. Thus, DCNN learn edges, arcs, arcs segments and the other shape features of the spectrum. Thus, the method disclosed enables converting a spectral signature to a shape, and then this shape is decomposed using hierarchical features learned at different convolution layers of the disclosed DCNN at different levels.
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公开(公告)号：US10586104B2
公开(公告)日：2020-03-10
申请号：US16041438
申请日：2018-07-20
Applicant: Tata Consultancy Services Limited
Abstract: System and method of the present disclosure provide a linguistic approach to image processing. Prior art focused on extracting well-defined single objects occupying large portion of an image area. However, there was no focus on higher level semantics or distribution of object categories within the image. In contrast to imagery by handheld devices, remotely sensed data contains numerous objects because of relative large coverage and distribution over objects is critical to analyzing such large coverage. Accordingly, in the present disclosure, a generative statistical model is defined wherein an aerial image is modelled as a collection of the one or more themes and each of the one or more themes is modelled as a collection of object categories. The model automatically adapts to a scale of the aerial image and appropriately identifies the themes which may be used for applications including monitoring land use, infrastructure management and the like.
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Patent Agency Ranking
公开(公告)号：US20150006531A1
公开(公告)日：2015-01-01
申请号：US14188979
申请日：2014-02-25
Applicant: Tata Consultancy Services Limited
Inventor： Shailesh Shankar Deshpande , Girish Keshav Palshikar , Athiappan G
IPC: G06F17/30
CPC classification number: G06F17/30784
Abstract: Disclosed is a method and system for creating labels for cluster in computing environment. The system comprises receiving module, candidate items selector, combination array generator, coverage value analyzer, candidate pair selector, unique word filter and cluster label selector. Receiving module receives input data and candidate items selector selects candidate items occurring repetitively using n-gram technique to generate list of candidate items with frequency of occurrence. Combination array generator selects candidate items to populate two-dimensional array wherein each array element represents pair of n-gram. Coverage value analyzer determines coverage value for each pair of n-gram from array. Candidate pair selector selects pairs of n-gram from two-dimensional array to process and generate list of candidate pairs. The unique word filter determines number of unique words in each candidate pair. Cluster label selector sorts list of candidate pairs using coverage value and number of unique words to select cluster label.
Abstract translation: 公开了一种用于在计算环境中创建集群标签的方法和系统。 系统包括接收模块，候选项选择器，组合阵列发生器，覆盖值分析器，候选对选择器，唯一字滤波器和簇标签选择器。 接收模块接收输入数据，候选项选择器选择使用n-gram技术重复出现的候选项，以产生出现频率的候选项目列表。 组合阵列发生器选择候选项来填充二维数组，其中每个数组元素表示一对n-gram。 覆盖值分析器确定阵列中每对n-gram的覆盖值。 候选对选择器从二维数组中选择一对n-gram进行处理并生成候选对列表。 唯一字过滤器确定每个候选对中唯一字的数量。 群集标签选择器使用覆盖值和唯一字数选择群集标签来排序候选对的列表。
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公开(公告)号：US20220138481A1
公开(公告)日：2022-05-05
申请号：US17201710
申请日：2021-03-15
Applicant: Tata Consultancy Services Limited
Abstract: Hyperspectral data associated with hyperspectral images received for any Region of Interest (ROI) is in form of number of pixel vectors. Unlike conventional methods in the art that treat this pixel vector as a time series, the embodiments herein provide a method and system that analyzes the pixel vectors by transforming the pixel vector into two-dimensional spectral shape space and then perform convolution over the image of graph thus formed. Learning from pixel vectors directly may not capture the spectral details efficiently. The intuition is to learn the spectral features as represented by the shape of a spectrum or in other words the features which a spectroscopy expert uses to interpret the spectrum. Method and system disclosed converts the pixel vector into image and provides a DCNN architecture that is built for processing 2D visual representation of the pixel vectors to learn spectral and classify the pixels. Thus, DCNN now learn edges, arcs, arcs segments and the other shape features of the spectrum
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公开(公告)号：US10964076B2
公开(公告)日：2021-03-30
申请号：US16504196
申请日：2019-07-05
Applicant: Tata Consultancy Services Limited
Inventor： Jayavardhana Rama Gubbi Lakshminarasimha , Karthik Seemakurthy , Sandeep Nk , Ashley Varghese , Shailesh Shankar Deshpande , Mariaswamy Girish Chandra , Balamuralidhar Purushothaman , Angshul Majumdar
Abstract: This disclosure relates generally to image processing, and more particularly to method and system for image reconstruction using deep dictionary learning (DDL). The system collects the degraded image as test image and processes the test image to extract sparse features from the test image, at different levels, using dictionaries. The extracted sparse features and data from the dictionaries are used by the system to reconstruct the HR image corresponding to the test image.
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公开(公告)号：US10949762B2
公开(公告)日：2021-03-16
申请号：US15271913
申请日：2016-09-21
Applicant: Tata Consultancy Services Limited
Abstract: The present disclosure provides a method and a system for optimizing Hidden Markov Model based land change prediction. Firstly, remotely sensed data is pre-processed and classified into a plurality of land use land cover classes (LULC). Then socio-economic driver variables data for a pre-defined interval of time are provided from a database. A Hidden Markov Model (HMM) is defined with LULC as hidden states and socio-economic driver variables data as observations and trained for generating a MINI state transition probability matrix. Again the defined MINI is trained by taking input data from scenario based temporal variables to generate another set of HMM state transition probability matrix. The generated MINI state transition probability matrix is then integrated with a spatio-temporal model to obtain an integrated model for predicting LULC changes to generate at least one prediction image.
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公开(公告)号：US10599946B2
公开(公告)日：2020-03-24
申请号：US15869311
申请日：2018-01-12
Applicant: Tata Consultancy Services Limited
Inventor： Jayavardhana Rama Gubbi Lakshminarasimha , Karthikeyan Vaiapury , Mariswamy Girish Chandra , Balamuralidhar Purushothaman , Shailesh Shankar Deshpande
Abstract: A system and method for identifying real time change in a scene of an unknown environment using an unmanned vehicle. In the context of unmanned vehicle navigation, it is critical to calculate the saliency map in real time and employ them in scene understanding. This will reduce the search space and ensure that the process is quicker. A domain specific ontology is created and a saliency model is developed. The saliency model detects key domain specific regions of interest in two consecutive images. The regions of interest is used for registration and change detection. The system is detecting the change by using visual saliency as an abstract feature that is developed in the environment. Probability of change is derived using the salient maps of the two images.
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