Tracking of objects with known color signature - ELITECH 20
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My presentation on conference ELITECH 2011. It is on tracking colored objects. Presentation and paper won the award for best contribution in area of computer science from Slovak IT SAV society.
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![Tracking of objects with known color signature Lukáš Tencer [email_address]](https://image.slidesharecdn.com/prezent-111207174844-phpapp02/85/Tracking-of-objects-with-known-color-signature-ELITECH-20-1-320.jpg){kind=tracking}
![Content ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Cahall Final Intern Presentation
The document proposes improving object detection and recognition capabilities. It discusses challenges with current methods like different object sizes and color variations. The objectives are to build a module that can learn and detect objects without a sliding box or datastore. A high-level design approach is outlined using techniques like contouring, BING, sliding box, and feature selection methods. The design considers optimal feature selection, dimensionality reduction, and classification algorithms to function in real-time.
Automated features extraction from satellite images.
This is the final year civil engineering project presentation in which different features i.e. Buildings, Road Network, Vegetation and Water are extracted automatically from satellite images with the help of Ecognition software. We have done our analysis on satellite images of sikar, rajasthan. In this project object based image analysis (OBIA) approach are used.
Ijdmta v1i1
This document discusses analyzing cognitive load to classify learning systems for special needs education using data mining.
It begins by introducing the need for special education systems to accommodate individual learning challenges. The paper then aims to observe six types of cognitive load (mental, physical, temporal, performance, effort, frustration) across ten different memory and learning systems.
The key points are:
1) Cognitive load is observed for long term, short term, working, instant, responsive, process, recollect, reference, instruction and action memory systems.
2) Classification analysis is used to predict patterns for specific learning challenges based on generalized and specialized cognitive load properties.
3) The goal is to classify learning systems according to
Survey on Content Based Image Retrieval
Invention of digital technology has lead to increase in the number of images that can be stored in digital format. So searching and retrieving images in large image databases has become more challenging. From the last few years, Content Based Image Retrieval (CBIR) gained increasing attention from researcher. CBIR is a system which uses visual features of image to search user required image from large image
database and user’s requests in the form of a query image. Important features of images are colour, texture and shape which give detailed information about the image. CBIR techniques using different feature extraction techniques are discussed in this paper.
Content Based Image Retrieval
Content-based image retrieval (CBIR) uses visual image content to search large image databases according to user needs. CBIR systems represent images by extracting features related to color, shape, texture, and spatial layout. Features are extracted from regions of the image and compared to features of images in the database to find the most similar matches. CBIR has applications in medical imaging, fingerprints, photo collections, and more. Techniques include representing images with histograms of color and texture features extracted through transforms.
Knowledge-based Fusion for Image Tampering Localization
Presentation of a new approach for the fusion of outputs from multiple tampering localization algorithms at the AIAI 2020 conference (online).
Remotely Sensed Image (RSI) Analysis for feature extraction using Color map I...
Remote Sensing is the science and art of acquiring
information (spectral, spatial, and temporal) about material
objects, area, or phenomenon, without coming into physical
contact with them ad plays a significant role in feature
extraction. In the present paper, implementation of color
mapping index method is analyzed to extract features from RSI
in spectral domain. Color indexing is applied after fixing the
index value to the pixels of selected ROI (Region of Interest)
of RSI and there by clustering based on these index values.
Color mapping, which is also called tone mapping can be used
to apply color transformations on the final image colors of the
ROI. The process of color map indexing is a color map
approximation approach on RSI for feature extraction includes
designing appropriate algorithm, its implementation and
discussion on the results of such implementation on ROI.
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APPLYING R-SPATIOGRAM IN OBJECT TRACKING FOR OCCLUSION HANDLING
APPLYING R-SPATIOGRAM IN OBJECT TRACKING FOR OCCLUSION HANDLING
GEOGRAPHIC MAPS CLASSIFICATION BASED ON L*A*B COLOR SYSTEM
GEOGRAPHIC MAPS CLASSIFICATION BASED ON L*A*B COLOR SYSTEM
JPM1407 Exposing Digital Image Forgeries by Illumination Color Classification
JPM1407 Exposing Digital Image Forgeries by Illumination Color Classification
A Linear-Algebraic Technique with an Application in Semantic Image Retrieval
A Linear-Algebraic Technique with an Application in Semantic Image Retrieval
A brief introduction to extracting information from images
A brief introduction to extracting information from images
Automatic Building detection for satellite Images using IGV and DSM
Automatic Building detection for satellite Images using IGV and DSM
Spot the Dog: An overview of semantic retrieval of unannotated images in the ...
Spot the Dog: An overview of semantic retrieval of unannotated images in the ...
Automated features extraction from satellite images.
Automated features extraction from satellite images.
Knowledge-based Fusion for Image Tampering Localization
Knowledge-based Fusion for Image Tampering Localization
Remotely Sensed Image (RSI) Analysis for feature extraction using Color map I...
Remotely Sensed Image (RSI) Analysis for feature extraction using Color map I...
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Dilation and erosion
Dilation and erosion are two fundamental morphological operations used to modify binary images. Dilation adds pixels to object boundaries in an image, while erosion removes pixels from object boundaries. The number of pixels added or removed depends on the size and shape of the structuring element, which is a matrix of 1's and 0's used to probe the image and determine changes.
color tracking robot
This document describes the design of a color tracking robot project. It uses a webcam and image processing software to detect a target color on a human. An ultrasonic sensor helps avoid obstacles. A microcontroller controls DC motors and receives input from sensors. The robot can follow and carry loads for a soldier to lighten their burden. Future extensions could improve identification through face or body recognition and use IR cameras for better accuracy. The goal is a low-cost robot for applications like military transport or assistance for the elderly.
Design and implementation of color tracking method on Chess Robot Using Camera
This thesis aims to test whether the color tracking system can be implemented on a chess robot. Color tracking aims to locate the position of chess. This identification process using color as a reference. Each chess piece has a different color. Input from the image in the form of a camera that is placed on a chessboard. Testing is done by changing the light and the angle of the camera. Then tested whether the input can detect and recognize chess. The results of this test, the computer can recognize chess. This input will be implemented on the robot chess.
Glas Trösch - Challenges of using glass in the Alpine regions
High up in the Alps, the architecture is often characterized by a traditional method of construction that has few transparent surfaces and, in order to provide protection against the extreme weather conditions, tends to be of rather reserved nature. This is where contemporary façade glazing opens up new opportunities: The transparent material not only offers greater creative freedom but also enables designers to realise bright and inviting rooms which allow for far-reaching views of the mountains and create the optimum conditions for the successful use of the buildings from the tourism aspect. As a Swiss company, Glas Trösch has extensive experience in the use of glass at extreme altitudes and knows the special requirements demanded of the material at these exposed locations.
Mouse simulation using coloured tape
The document proposes a novel approach to simulate mouse functions using only a webcam and computer vision techniques. Two colored tapes would be worn on the fingers to detect hand gestures for controlling mouse movements and clicks. The yellow tape on the index finger would control cursor position while the distance between the yellow and red tapes would determine click events. Left clicks would occur when the thumb tape nears the index finger tape, right clicks from pausing in position, and double clicks from pausing both tapes in position. This vision-based mouse simulation could revolutionize human-computer interaction by eliminating physical devices.
Multi Object Tracking | Final Defense | ID 103001
Slide for Multi Object Tracking by Md. Minhazul Haque, Rajshahi University of Engineering and Technology
* Objectives
* Object Tracking
* Applications
* Methodology
* Implementation
* Experiment Result
* Performance Analysis
* Future Work
* References
Multi Object Tracking | Presentation 1 | ID 103001
Slide for Multi Object Tracking by Md. Minhazul Haque, Rajshahi University of Engineering and Technology
* Object
* Object Tracking
* Application
* Background Study
* How it works
* Multi-Object Tracking
* Solution
* Future Works
Colours
This document provides an overview of colour theory and models for application in multimedia. It describes how colour is perceived and named, then covers the RGB, CMYK, and HSV colour models. It discusses additive and subtractive colour synthesis. The document also addresses colour palettes, web colours, and colour wheels. Colour matching between RGB and CMYK is examined along with references for further information.
Color based image processing , tracking and automation using matlab
Image processing is a form of signal processing in which the input is an image, such as a photograph or video frame. The output of image processing may be either an image or, a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it. This project aims at processing the real time images captured by a Webcam for motion detection and Color Recognition and system automation using MATLAB programming.
In color based image processing we work with colors instead of object. Color provides powerful information for object recognition. A simple and effective recognition scheme is to represent and match images on the basis of color histograms.
Tracking refers to detection of the path of the color once the color based processing is done the color becomes the object to be tracked this can be very helpful in security purposes.
Automation refers to an automated system is any system that does not require human intervention. In this project I’ve automated the mouse that work with our gesture and do the desired tasks.
Real Time Object Tracking
This document summarizes several methods for real-time object detection and tracking in video sequences. Traditional methods like absolute differences and census transforms are compared to modern methods like KLT (Lucas-Kanade Technique) and Meanshift. Hardware requirements for real-time tracking like memory, frame rate, and processors are also discussed. The document provides examples of applications for object detection and tracking in traffic monitoring, surveillance, and mobile robotics.
Object tracking
The document discusses object tracking in computer vision. It begins with an introduction and overview of applications of object tracking. It then discusses object representation, detection, tracking algorithms and methodologies. It compares different tracking methods and provides an example of object tracking in MATLAB. Key steps in object tracking include object detection, tracking the detected objects across frames using algorithms like point tracking, kernel tracking and silhouette tracking. Common challenges with object tracking are also summarized.
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Design and implementation of color tracking method on Chess Robot Using Camera
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Multi Object Tracking | Presentation 1 | ID 103001
Multi Object Tracking | Presentation 1 | ID 103001
Color based image processing , tracking and automation using matlab
Color based image processing , tracking and automation using matlab
Similar to Tracking of objects with known color signature - ELITECH 20
Object tracking
Object tracking involves tracing the movement of objects in a video sequence. There are various object representation methods like points, shapes, and skeletons. Popular tracking algorithms include point tracking, kernel tracking, and silhouette tracking. Key steps are object detection, feature extraction, segmentation, and tracking. Common challenges are illumination changes, occlusions, and complex motions. The document compares methods like optical flow, mean shift, and feature-based tracking. In conclusion, object tracking has advanced but challenges remain like handling occlusions.
2D/Multi-view Segmentation and Tracking
1. The document discusses 2D/multi-view segmentation and tracking techniques for video analysis including spatial segmentation, object tracking across multiple views, and detecting unusual events based on trajectory analysis.
2. It describes applying segmentation to extract regions and objects from video frames then tracking them across multiple views using correspondence between views.
3. Techniques for detecting unusual events include modeling normal trajectories, extracting features from trajectories, and using SVM classification to detect deviations from normal behavior. Experiments show the approach can detect unusual events on new video sequences.
Flag segmentation, feature extraction & identification using support vector m...
Flag segmentation, feature extraction & identification using support vector m...R M Shahidul Islam Shahed
Develop a system that can identify flags embedded in photos of natural scenes.
Develop a system that can segment a flag portion automatically accurately.
Reduce the identification time and produce a good result.
Apply Support Vector Machine(SVM) to generate the correct Result.
F1063337
This document summarizes a research paper that presents a framework for detecting and tracking objects in real-time video based on color. The proposed methodology uses a webcam to capture video, performs color-based filtering and image processing to isolate the target object, and analyzes the object's motion over time to track its path. Key steps include Euclidean filtering to isolate the object's color, converting to grayscale for faster processing, contour extraction to delineate the object's shape, and analyzing metrics like the Hurst exponent and Lyapunov exponent to detect chaos in the object's motion over time. The goal is to develop an efficient and real-time system for color-based object detection and tracking.
International Journal of Engineering Research and Development
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Ijarcet vol-2-issue-4-1298-1303
This document discusses object tracking techniques in computer vision. It begins by defining object tracking as segmenting an object from video frames and observing its motion and position over time. There are several challenges to object tracking, including illumination changes, object occlusion, and camera motion. The document then describes two main approaches to object tracking: feature-based methods which extract image features to track objects, and kernel-based methods which represent objects using shapes and track their motion. It provides examples of kernel tracking methods like mean shift and discusses challenges like overlapping objects. In conclusion, the document implemented and compared mean shift, CAMShift and contour tracking algorithms for object tracking.
DEVELOPMENT OF AN ANDROID APPLICATION FOR OBJECT DETECTION BASED ON COLOR, SH...
Object detection and recognition is an important task in many computer vision applications. In this paper
an Android application was developed using Eclipse IDE and OpenCV3 Library. This application is able to
detect objects in an image that is loaded from the mobile gallery, based on its color, shape, or local
features. The image is processed in the HSV color domain for better color detection. Circular shapes are
detected using Circular Hough Transform and other shapes are detected using Douglas-Peucker
algorithm. BRISK (binary robust invariant scalable keypoints) local features were applied in the developed
Android application for matching an object image in another scene image. The steps of the proposed
detection algorithms are described, and the interfaces of the application are illustrated. The application is
ported and tested on Galaxy S3, S6, and Note1 Smartphones. Based on the experimental results, the
application is capable of detecting eleven different colors, detecting two dimensional geometrical shapes
including circles, rectangles, triangles, and squares, and correctly match local features of object and scene
images for different conditions. The application could be used as a standalone application, or as a part of
another application such as Robot systems, traffic systems, e-learning applications, information retrieval
and many others.
C04741319
The document presents a hybrid approach for detecting and recognizing text in images. It consists of three main steps:
1) Image partition using k-means clustering to segment text regions based on color information.
2) Character grouping to detect text characters within each text string based on character size differences and distance between characters.
3) Text recognition of detected characters using a neural network.
The proposed method was evaluated on a street view text dataset, achieving a precision of 0.83, recall of 0.93, and f-measure of 0.25 for text recognition. The approach efficiently and accurately detects and recognizes text with low false positives.
Object detection involves identifying and locating
1. The document discusses object tracking and moving object detection, including algorithms for tracking objects through successive image frames.
2. Object representation and feature selection are important for tracking, with common representations being points, shapes, silhouettes. Common features include color, edges, optical flow, and texture.
3. Moving object detection algorithms discussed include background subtraction, frame differencing, and edge detection. Advantages and disadvantages of these algorithms are also presented.
A Survey on Approaches for Object Tracking
This document summarizes various approaches for object tracking in video sequences. It discusses common object detection methods like temporal differencing, optical flow and background subtraction. For object representation, it describes shape-based, motion-based, color-based and texture-based approaches. For object tracking, it analyzes target representation and localization methods like blob tracking and mean shift, as well as filtering and data association approaches like the Kalman filter and particle filter. It provides comparisons between Kalman and particle filters, and between contour tracking and visual feature matching methods. The document concludes that further research could improve computational efficiency and decrease tracking time for diverse video content.
Object tracking a survey
This is a summary of Object Tracking Survey prested by the well renowned scholar Alper Yilmaz , Omer Javed and Mubarak Shah
Remote Sensing Image Scene Classification
Project to show satellite imagery prediction based on Deep Learning methods and prove that CNN+ BoVW <= BoCF.
AUTOMATIC DOMINANT REGION SEGMENTATION FOR NATURAL IMAGES
The document proposes an automatic dominant region segmentation algorithm for natural images. It begins with an introduction to image segmentation and its applications. A literature review covers previous work on color image segmentation techniques. The proposed algorithm first converts the input RGB image to grayscale, applies preprocessing like filtering, and performs edge detection. It then separates the foreground object from the background using thresholding. Dominant regions are identified and the segmented color image with boundaries is produced. Experimental results on benchmark datasets show the algorithm avoids over-segmentation compared to previous methods like JSEG. The conclusions state that color and texture are important for segmentation and the proposed method simple implements dominant region extraction.
Automatic dominant region segmentation for natural images
Image Segmentation segments an image into different homogenous regions. An efficient
semantic based image retrieval system divides the image into different regions separated by
color or texture sometimes even both. Features are extracted from the segmented regions and
are annotated automatically. Relevant images are retrieved from the database based on the
keywords of the segmented region In this paper, automatic image segmentation is proposed to
obtained dominant region of the input natural images. Dominant region are segmented and
results are obtained . Results are also recorded in comparison to JSEG algorithm
OBJECT DETECTION AND RECOGNITION: A SURVEY
The efficiency and quality of a feature descriptor are critical to the user experience of many computer vision applications. However, the existing descriptors are either too computationally expensive to achieve real-time performance, or not sufficiently distinctive to identify correct matches from a large database with various transformations. In this paper, we propose a highly efficient and distinctive binary descriptor, called local difference binary (LDB). LDB directly computes a binary string for an image patch using simple intensity and gradient difference tests on pair wise grid cells within the patch. A multiple-gridding strategy and a salient bit-selection method are applied to capture the distinct patterns of the patch at different spatial granularities. Experimental results demonstrate that compared to the existing state-of-the-art binary descriptors, primarily designed for speed, LDB has similar construction efficiency, while achieving a greater accuracy and faster speed for mobile object recognition and tracking tasks.
Multimedia searching
Slides for the Course on Web Science (fall 2011) of Jim Hendler at RPI (USA) by Laura Papaleo. Digital Shapes content-based searching and retrieval.
Real time implementation of object tracking through
This document presents a methodology for real-time object tracking using a webcam. It combines Prewitt edge detection for object detection and Kalman filtering for tracking. Prewitt edge detection is used to detect the edges of the moving object in each video frame. Then, Kalman filtering is used to track the detected object across subsequent frames by predicting its location. Experiments show the approach can efficiently track objects under deformation, occlusion, and can track multiple objects simultaneously. The combination of Prewitt edge detection and Kalman filtering provides an effective method for real-time object tracking.
Content based image retrieval based on shape with texture features
This document describes a content-based image retrieval system that extracts shape and texture features from images. It uses the HSV color space and wavelet transform for feature extraction. Color features are extracted by quantizing the H, S, and V components of HSV into unequal intervals based on human color perception. Texture features are extracted using wavelet transforms. The color and texture features are then combined to form a feature vector for each image. During retrieval, the similarity between a query image and images in the database is measured using the Euclidean distance between their feature vectors. The results show that retrieving images using HSV color features provides more accurate results and faster retrieval times compared to using RGB color features.
OBJECT DETECTION FOR SERVICE ROBOT USING RANGE AND COLOR FEATURES OF AN IMAGE
This document summarizes an approach for object detection using both range and color image features. The proposed method first generates hypotheses for objects in a range image using a generative model (pLSA) applied to bag-of-visual-words representing 3D shape. It then verifies the hypotheses using an SVM classifier combining 3D shape features from the range image and color appearance features from the corresponding area of the color image. The approach was tested on images containing multiple objects acquired using both a range sensor and color camera.
Object Detection for Service Robot Using Range and Color Features of an Image
In real-world applications, service robots need to locate and identify objects in a scene. A range sensor provides a robust estimate of depth information, which is useful to accurately locate objects in a scene. On the other hand, color information is an important property for object recognition task. The objective of this paper is to detect and localize multiple objects within an image using both range and color features. The proposed method uses 3D shape features to generate promising hypotheses within range images and verifies these hypotheses by using features obtained from both range and color images.
Similar to Tracking of objects with known color signature - ELITECH 20 (20)
Flag segmentation, feature extraction & identification using support vector m...
Flag segmentation, feature extraction & identification using support vector m...
International Journal of Engineering Research and Development
International Journal of Engineering Research and Development
DEVELOPMENT OF AN ANDROID APPLICATION FOR OBJECT DETECTION BASED ON COLOR, SH...
DEVELOPMENT OF AN ANDROID APPLICATION FOR OBJECT DETECTION BASED ON COLOR, SH...
Object detection involves identifying and locating
Object detection involves identifying and locating
AUTOMATIC DOMINANT REGION SEGMENTATION FOR NATURAL IMAGES
AUTOMATIC DOMINANT REGION SEGMENTATION FOR NATURAL IMAGES
Automatic dominant region segmentation for natural images
Automatic dominant region segmentation for natural images
Real time implementation of object tracking through
Real time implementation of object tracking through
Content based image retrieval based on shape with texture features
Content based image retrieval based on shape with texture features
OBJECT DETECTION FOR SERVICE ROBOT USING RANGE AND COLOR FEATURES OF AN IMAGE
OBJECT DETECTION FOR SERVICE ROBOT USING RANGE AND COLOR FEATURES OF AN IMAGE
Object Detection for Service Robot Using Range and Color Features of an Image
Object Detection for Service Robot Using Range and Color Features of an Image
More from Lukas Tencer
Semi-Supervised Learning
Review presentation about Semi-Supervised techniques in Machine Learning. Presentation was done as part of Montreal Data series.
ICRA: Intelligent Platform for Collaboration and Interaction
Presentation for a class at Polytechnique Montreal. First halve focuses on presentation of the platform, second halve focuses on presentation of algorithms.
Introduction to Probability
This document introduces fundamental concepts in probability that are important for computer vision models, including:
1) Random variables represent uncertain quantities that can take on different values, described by probability distributions.
2) Joint, marginal, and conditional probabilities describe the relationships between multiple random variables and allow computing the probability of one variable given information about others.
3) Bayes' rule provides a way to calculate conditional probabilities and invert relationships between cause and effect.
4) Independence and expectation define how variables interact and allow calculating average values over probability distributions. These concepts form the basis for probabilistic models in computer vision.
Common Probability Distibution
This document discusses common probability distributions used in computer vision. It introduces the Bernoulli, beta, categorical, and Dirichlet distributions which model useful quantities like binary outcomes and categorical variables. It then presents conjugate distributions that model the parameters of the first four distributions, such as the normal inverse gamma distribution for the univariate normal. The document explains that these conjugate pairs have a special relationship where the posterior takes the same form as the prior, allowing for efficient calculation in learning models.
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Presentation of paper "Large Scale Online Learning of Image Similarity Through Ranking" for Synchromedia Seminar on 15. 9. 2012.
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Our (me and Marta Reznakova) presentation of Slovakia at ETS (Ecole de Technologie Superieure) multicultural event.
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My presentation for course SYS821 "Pattern recognition and inspection" at ETS. This describes implementation of my project on topic "Web-based framework for online sketch-based image retrieval".
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
This is presentation done by me for ECSE626 "Statistical Computer Vision" at McGill University. It is presentation of a project inspired by paper "Supervised Learning of Semantic Classes for Image Annotation and Retrieval" from PAMI 2007. It presents my implementation of the paper and my achieved results.
Personal Career,Education and skills presentation, 2011
Lukas Tencer has worked as a test engineer, software engineer, lead programmer, and researcher between 2006-2011. He has a Bachelor's, Master's, and PhD in computer science and has worked on projects for universities and companies in Slovakia, Ireland, Netherlands, and Canada. His programming experience includes C++, Java, JavaScript, and other languages.
Introduction to Computer Graphics, lesson 1
Slides for first lesson of "Introduction to computer graphics" course, which I was teaching.
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Presentation on computer graphics systems used in mobile devices and on web. Overview of techniques with details and capabilities, with aim on 2D and 3D graphics. Presentation is in Slovak language.
Telnet and SSH
Presentation from my early school time on telnet and SSH. Basic overview of techniques and technical details. In cooperation with Lukas Apalovic. Presentation is in slovak
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ICRA: Intelligent Platform for Collaboration and Interaction
ICRA: Intelligent Platform for Collaboration and Interaction
Large Scale Online Learning of Image Similarity Through Ranking
Large Scale Online Learning of Image Similarity Through Ranking
Web-based framework for online sketch-based image retrieval
Web-based framework for online sketch-based image retrieval
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
Personal Career,Education and skills presentation, 2011
Personal Career,Education and skills presentation, 2011
Tracking of objects with known color signature - ELITECH 20
- 1. Tracking of objects with known color signature Lukáš Tencer [email_address]
- 13. Thank you for you attention
- 14. Questions ?