Machine learning Image classification for identificationDhruveeHalvadiya
mage classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labeled example photos. Early computer vision models relied on raw pixel data as the input to the model.To model objects more flexibly, classic computer vision models added new features derived from pixel data, such as color histograms, textures, and shapes.
3-D Face Recognition Using Improved 3D Mixed TransformCSCJournals
This paper deals with the using of Improved 3D Mixed Transform (3D-IMT) for face recognition problem. The mixed transform consists; Fourier based 3D radon transform plus 1-D Wavelet transform (which is also known as 3D Ridgelet transform). The Mixed Transform is improved by using Particle swarm optimization ( PSO) , the improvement involves the selection of the best of directions for smart rectangle-to-polar transform as a part of the 3D Radon Transformation. The 3D-IMT is applied to the 3D representation of face images, and yields a few number of features, these features is projected into the maximized projection that achieves good recognition rate using the Linear Discriminant Analysis (LDA).
Face detection using the 3 x3 block rank patterns of gradient magnitude imagessipij
Face detection locates faces prior to various face-
related applications. The objective of face detecti
on is to
determine whether or not there are any faces in an
image and, if any, the location of each face is det
ected.
Face detection in real images is challenging due to
large variability of illumination and face appeara
nces.
This paper proposes a face detection algorithm usin
g the 3×3 block rank patterns of gradient magnitude
images and a geometrical face model. First, the ill
umination-corrected image of the face region is obt
ained
using the brightness plane that is produced using t
he locally minimum brightness of each block. Next,
the
illumination-corrected image is histogram equalized
, the face region is divided into nine (3×3) blocks
, and
two directional (horizontal and vertical) gradient
magnitude images are computed, from which the 3×3
block rank patterns are obtained. For face detectio
n, using the FERET and GT databases three types of
the
3×3 block rank patterns are a priori determined as
templates based on the distribution of the sum of t
he
gradient magnitudes of each block in the face candi
date region that is also composed of nine (3×3) blo
cks.
The 3×3 block rank patterns roughly classify whethe
r the detected face candidate region contains a fac
e or
not. Finally, facial features are detected and used
to validate the face model. The face candidate is
validated as a face if it is matched with the geome
trical face model. The proposed algorithm is tested
on the
Caltech database images and real images. Experiment
al results with a number of test images show the
effectiveness of the proposed algorithm.
Detecing facial keypoints is a very challenging problem. Facial features vary greatly from one individual to another, and even for a single individual, there is a large amount of variation due to 3D pose, size, position, viewing angle, and illumination conditions. Computer vision research has come a long way in addressing these difficulties, but there remain many oppurtunities for improvement.
In this presentation we have used different methods to recognize facial keypoints and compared their RMSE (Root Mean Square Errors) to get better results and accuracy.
Machine learning Image classification for identificationDhruveeHalvadiya
mage classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labeled example photos. Early computer vision models relied on raw pixel data as the input to the model.To model objects more flexibly, classic computer vision models added new features derived from pixel data, such as color histograms, textures, and shapes.
3-D Face Recognition Using Improved 3D Mixed TransformCSCJournals
This paper deals with the using of Improved 3D Mixed Transform (3D-IMT) for face recognition problem. The mixed transform consists; Fourier based 3D radon transform plus 1-D Wavelet transform (which is also known as 3D Ridgelet transform). The Mixed Transform is improved by using Particle swarm optimization ( PSO) , the improvement involves the selection of the best of directions for smart rectangle-to-polar transform as a part of the 3D Radon Transformation. The 3D-IMT is applied to the 3D representation of face images, and yields a few number of features, these features is projected into the maximized projection that achieves good recognition rate using the Linear Discriminant Analysis (LDA).
Face detection using the 3 x3 block rank patterns of gradient magnitude imagessipij
Face detection locates faces prior to various face-
related applications. The objective of face detecti
on is to
determine whether or not there are any faces in an
image and, if any, the location of each face is det
ected.
Face detection in real images is challenging due to
large variability of illumination and face appeara
nces.
This paper proposes a face detection algorithm usin
g the 3×3 block rank patterns of gradient magnitude
images and a geometrical face model. First, the ill
umination-corrected image of the face region is obt
ained
using the brightness plane that is produced using t
he locally minimum brightness of each block. Next,
the
illumination-corrected image is histogram equalized
, the face region is divided into nine (3×3) blocks
, and
two directional (horizontal and vertical) gradient
magnitude images are computed, from which the 3×3
block rank patterns are obtained. For face detectio
n, using the FERET and GT databases three types of
the
3×3 block rank patterns are a priori determined as
templates based on the distribution of the sum of t
he
gradient magnitudes of each block in the face candi
date region that is also composed of nine (3×3) blo
cks.
The 3×3 block rank patterns roughly classify whethe
r the detected face candidate region contains a fac
e or
not. Finally, facial features are detected and used
to validate the face model. The face candidate is
validated as a face if it is matched with the geome
trical face model. The proposed algorithm is tested
on the
Caltech database images and real images. Experiment
al results with a number of test images show the
effectiveness of the proposed algorithm.
Detecing facial keypoints is a very challenging problem. Facial features vary greatly from one individual to another, and even for a single individual, there is a large amount of variation due to 3D pose, size, position, viewing angle, and illumination conditions. Computer vision research has come a long way in addressing these difficulties, but there remain many oppurtunities for improvement.
In this presentation we have used different methods to recognize facial keypoints and compared their RMSE (Root Mean Square Errors) to get better results and accuracy.
Skin colour information and Haar feature based Face DetectionIJERA Editor
In today’s world security of data, person and information is very important aspects.So biometric systems for
user authentication are becoming increasingly popular due to the security control requirement in identity
verification, access control, and surveillance applications. For authentication various recognition techniques are
used e.g. vein pattern recognition, face recognition. For face recognition accurate face detection is primary need.
Here we present two different approaches for face detection. First face detection approach is based on skin
colour detection. Second approach is Haar feature based face detection.
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.
A Novel Mathematical Based Method for Generating Virtual Samples from a Front...CSCJournals
This paper deals with one sample face recognition which is a new challenging problem in pattern recognition. In the proposed method, the frontal 2D face image of each person divided to some sub-regions. After computing the 3D shape of each sub-region, a fusion scheme is applied on sub-regions to create a total 3D shape for whole face image. Then, 2D face image is added to the corresponding 3D shape to construct 3D face image. Finally by rotating the 3D face image, virtual samples with different views are generated. Experimental results on ORL dataset using nearest neighbor as classifier reveal an improvement about 5% in recognition rate for one sample per person by enlarging training set using generated virtual samples. Compared with other related works, the proposed method has the following advantages: 1) only one single frontal face is required for face recognition and the outputs are virtual images with variant views for each individual 2) need only 3 key points of face (eyes and nose) 3) 3D shape estimation for generating virtual samples is fully automatic and faster than other 3D reconstruction approaches 4) it is fully mathematical with no training phase and the estimated 3D model is unique for each individual.
Fully Automatic Facial Feature Point Detection Using Gabor Feature Based Boos...Yen Ho
This is a key paper : Fully Automatic Facial Feature Point Detection Using Gabor Feature Based Boosted Classifiers - face detection (100%) & feature extraction(93%) for expressionless faces
Histogram Gabor Phase Pattern and Adaptive Binning Technique in Feature Selec...CSCJournals
The aim of this paper is to develop a robust system for face recognition by using Histogram Gabor Phase Pattern (HGPP) and adaptive binning technique. Gabor wavelet function is used for representing the features of the image both in frequency and orientation level. The huge feature space created by Gabor wavelet is classified by using adaptive binning technique. The unused bin spaces are used. As a result of which, the size of the space is drastically reduced and high quality HGPP created. It is due to this approach, the computation complexity and the time taken for the process is reduced and the recognition rate of the face improved. The significance of this system is its compatibility in yielding best results in the face recognition with major factors of a face image. The system is verified with FERET database and the results are compared with those of the existing methods.
Establishment of an Efficient Color Model from Existing Models for Better Gam...CSCJournals
Human vision is an important factor in the areas of image processing. Research has been done for years to make automatic image processing but still human intervention can not be denied and thus better human intervention is necessary. Two most important points are required to improve human vision which are light and color. Gamma encoder is the one which helps to improve the properties of human vision and thus to maintain visual quality gamma encoding is necessary.
It is to mention that all through the computer graphics RGB (Red, Green, and Blue) color space is vastly used. Moreover, for computer graphics RGB color space is called the most established choice to acquire desired color. RGB color space has a great effort on simplifying the design and architecture of a system. However, RGB struggles to deal efficiently for the images those belong to the real-world.
Images are captured using cameras, videos and other devices using different magnifications. In most cases during processing, in compare to the original outlook the images appear either dark or bright in contrast. Human vision affects and thus poor quality image analysis may occur. Consequently this poor manual image analysis may have huge difference from the computational image analysis outcome. Question may arise here why we will use gamma encoding when histogram equalization or histogram normalization can enhance images. Enhancing images does not improve human visualization quality all the time because sometimes it brightens the image quality when it is needed to darken and vice-versa. Human vision reflects under universal illumination environment (not pitch black or blindingly bright) thus follows an approximate gamma or power function. Hence, this is not a good idea to brighten images all the time when better human visualization can be obtained while darkening the images. Better human visualization is important for manual image processing which leads to compare the outcome with the semiautomated or automated one. Considering the importance of gamma encoding in image processing we propose an efficient color model which will help to improve visual quality for manual processing as well as will lead analyzers to analyze images automatically for comparison and testing purpose.
De duplication of entities with-in a cluster using image matchingSaurabh Singh
The methodology involves converting a particular face into numbers. For every image the algorithm first detects the face and then perform the facial landmarks.
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.
Face Hallucination using Eigen Transformation in Transform DomainCSCJournals
Faces often appear very small in surveillance imagery because of the wide fields of view that are typically used and the relatively large distance between the cameras and the scene. In applications like face recognition, face detection etc. resolution enhancement techniques are therefore generally essential. Super resolution is the process of determining and adding missing high frequency information in the image to improve the resolution. It is highly useful in the areas of recognition, identification, compression, etc. Face hallucination is a subset of super resolution. This work is intended to enhance the visual quality and resolution of a facial image. It focuses on the eigen transform based face super resolution techniques in transform domain. Advantage of eigen transformation based technique is that, it does not require iterative optimization techniques and hence comparatively faster. Eigen transform is performed in wavelet transform and discrete cosine transform domains and the results are presented. The results establish the fact that the eigen transform is efficient in transform domain also and thus it can be directly applied with slight modifications on the compressed images.
Face Recognition using Improved FFT Based Radon by PSO and PCA TechniquesCSCJournals
Abstract Face Recognition is one of the problems which can be handled very well using a Hybrid technique or mixed transform rather than single technique, it is a very well in terms of a good performance and a large size of the problem. In this paper we represent the using of the Fourier-Based Radon Transform which is improved by the Particle Swarm Optimization (PSO). PSO in this research is used to select the optimum directions (projection angles) that achieve a very high recognition rate and a fast computation. The number of directions selected using PSO is less than the number required by ordinary Radon. This leads to a small number of features. These number of features are reduced farther using PCA to produce a low number of features which used to represent faces in the database. Our method has been applied to ORL Database and achieves 100% recognition rate.
Face Alignment Using Active Shape Model And Support Vector MachineCSCJournals
The Active Shape Model (ASM) is one of the most popular local texture models for face alignment. It applies in many fields such as locating facial features in the image, face synthesis, etc. However, the experimental results show that the accuracy of the classical ASM for some applications is not high. This paper suggests some improvements on the classical ASM to increase the performance of the model in the application: face alignment. Four of our major improvements include: i) building a model combining Sobel filter and the 2-D profile in searching face in image; ii) applying Canny algorithm for the enhancement edge on image; iii) Support Vector Machine (SVM) is used to classify landmarks on face, in order to determine exactly location of these landmarks support for ASM; iv) automatically adjust 2-D profile in the multi-level model based on the size of the input image. The experimental results on CalTech face database and Technical University of Denmark database (imm_face) show that our proposed improvement leads to far better performance.
AN ILLUMINATION INVARIANT FACE RECOGNITION USING 2D DISCRETE COSINE TRANSFORM...ijcsit
Automatic face recognition performance is affected due to the head rotations and tilt, lighting intensity and
angle, facial expressions, aging and partial occlusion of face using Hats, scarves, glasses etc.In this paper,
illumination normalization of face images is done by combining 2D Discrete Cosine Transform and
Contrast Limited Adaptive Histogram Equalization. The proposed method selects certain percentage of
DCT coefficients and rest is set to 0. Then, inverse DCT is applied which is followed by logarithm
transform and CLAHE. Thesesteps create illumination invariant face image, termed as ‘DCT CLAHE’
image. The fisher face subspace method extracts features from ‘DCT CLAHE’ imageand features are
matched with cosine similarity. The proposed method is tested in AR database and performance measures
like recognition rate, Verification rate at 1% FAR and Equal Error Rate are computed. The experimental
results shows high recognition rate in AR database.
Skin colour information and Haar feature based Face DetectionIJERA Editor
In today’s world security of data, person and information is very important aspects.So biometric systems for
user authentication are becoming increasingly popular due to the security control requirement in identity
verification, access control, and surveillance applications. For authentication various recognition techniques are
used e.g. vein pattern recognition, face recognition. For face recognition accurate face detection is primary need.
Here we present two different approaches for face detection. First face detection approach is based on skin
colour detection. Second approach is Haar feature based face detection.
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.
A Novel Mathematical Based Method for Generating Virtual Samples from a Front...CSCJournals
This paper deals with one sample face recognition which is a new challenging problem in pattern recognition. In the proposed method, the frontal 2D face image of each person divided to some sub-regions. After computing the 3D shape of each sub-region, a fusion scheme is applied on sub-regions to create a total 3D shape for whole face image. Then, 2D face image is added to the corresponding 3D shape to construct 3D face image. Finally by rotating the 3D face image, virtual samples with different views are generated. Experimental results on ORL dataset using nearest neighbor as classifier reveal an improvement about 5% in recognition rate for one sample per person by enlarging training set using generated virtual samples. Compared with other related works, the proposed method has the following advantages: 1) only one single frontal face is required for face recognition and the outputs are virtual images with variant views for each individual 2) need only 3 key points of face (eyes and nose) 3) 3D shape estimation for generating virtual samples is fully automatic and faster than other 3D reconstruction approaches 4) it is fully mathematical with no training phase and the estimated 3D model is unique for each individual.
Fully Automatic Facial Feature Point Detection Using Gabor Feature Based Boos...Yen Ho
This is a key paper : Fully Automatic Facial Feature Point Detection Using Gabor Feature Based Boosted Classifiers - face detection (100%) & feature extraction(93%) for expressionless faces
Histogram Gabor Phase Pattern and Adaptive Binning Technique in Feature Selec...CSCJournals
The aim of this paper is to develop a robust system for face recognition by using Histogram Gabor Phase Pattern (HGPP) and adaptive binning technique. Gabor wavelet function is used for representing the features of the image both in frequency and orientation level. The huge feature space created by Gabor wavelet is classified by using adaptive binning technique. The unused bin spaces are used. As a result of which, the size of the space is drastically reduced and high quality HGPP created. It is due to this approach, the computation complexity and the time taken for the process is reduced and the recognition rate of the face improved. The significance of this system is its compatibility in yielding best results in the face recognition with major factors of a face image. The system is verified with FERET database and the results are compared with those of the existing methods.
Establishment of an Efficient Color Model from Existing Models for Better Gam...CSCJournals
Human vision is an important factor in the areas of image processing. Research has been done for years to make automatic image processing but still human intervention can not be denied and thus better human intervention is necessary. Two most important points are required to improve human vision which are light and color. Gamma encoder is the one which helps to improve the properties of human vision and thus to maintain visual quality gamma encoding is necessary.
It is to mention that all through the computer graphics RGB (Red, Green, and Blue) color space is vastly used. Moreover, for computer graphics RGB color space is called the most established choice to acquire desired color. RGB color space has a great effort on simplifying the design and architecture of a system. However, RGB struggles to deal efficiently for the images those belong to the real-world.
Images are captured using cameras, videos and other devices using different magnifications. In most cases during processing, in compare to the original outlook the images appear either dark or bright in contrast. Human vision affects and thus poor quality image analysis may occur. Consequently this poor manual image analysis may have huge difference from the computational image analysis outcome. Question may arise here why we will use gamma encoding when histogram equalization or histogram normalization can enhance images. Enhancing images does not improve human visualization quality all the time because sometimes it brightens the image quality when it is needed to darken and vice-versa. Human vision reflects under universal illumination environment (not pitch black or blindingly bright) thus follows an approximate gamma or power function. Hence, this is not a good idea to brighten images all the time when better human visualization can be obtained while darkening the images. Better human visualization is important for manual image processing which leads to compare the outcome with the semiautomated or automated one. Considering the importance of gamma encoding in image processing we propose an efficient color model which will help to improve visual quality for manual processing as well as will lead analyzers to analyze images automatically for comparison and testing purpose.
De duplication of entities with-in a cluster using image matchingSaurabh Singh
The methodology involves converting a particular face into numbers. For every image the algorithm first detects the face and then perform the facial landmarks.
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.
Face Hallucination using Eigen Transformation in Transform DomainCSCJournals
Faces often appear very small in surveillance imagery because of the wide fields of view that are typically used and the relatively large distance between the cameras and the scene. In applications like face recognition, face detection etc. resolution enhancement techniques are therefore generally essential. Super resolution is the process of determining and adding missing high frequency information in the image to improve the resolution. It is highly useful in the areas of recognition, identification, compression, etc. Face hallucination is a subset of super resolution. This work is intended to enhance the visual quality and resolution of a facial image. It focuses on the eigen transform based face super resolution techniques in transform domain. Advantage of eigen transformation based technique is that, it does not require iterative optimization techniques and hence comparatively faster. Eigen transform is performed in wavelet transform and discrete cosine transform domains and the results are presented. The results establish the fact that the eigen transform is efficient in transform domain also and thus it can be directly applied with slight modifications on the compressed images.
Face Recognition using Improved FFT Based Radon by PSO and PCA TechniquesCSCJournals
Abstract Face Recognition is one of the problems which can be handled very well using a Hybrid technique or mixed transform rather than single technique, it is a very well in terms of a good performance and a large size of the problem. In this paper we represent the using of the Fourier-Based Radon Transform which is improved by the Particle Swarm Optimization (PSO). PSO in this research is used to select the optimum directions (projection angles) that achieve a very high recognition rate and a fast computation. The number of directions selected using PSO is less than the number required by ordinary Radon. This leads to a small number of features. These number of features are reduced farther using PCA to produce a low number of features which used to represent faces in the database. Our method has been applied to ORL Database and achieves 100% recognition rate.
Face Alignment Using Active Shape Model And Support Vector MachineCSCJournals
The Active Shape Model (ASM) is one of the most popular local texture models for face alignment. It applies in many fields such as locating facial features in the image, face synthesis, etc. However, the experimental results show that the accuracy of the classical ASM for some applications is not high. This paper suggests some improvements on the classical ASM to increase the performance of the model in the application: face alignment. Four of our major improvements include: i) building a model combining Sobel filter and the 2-D profile in searching face in image; ii) applying Canny algorithm for the enhancement edge on image; iii) Support Vector Machine (SVM) is used to classify landmarks on face, in order to determine exactly location of these landmarks support for ASM; iv) automatically adjust 2-D profile in the multi-level model based on the size of the input image. The experimental results on CalTech face database and Technical University of Denmark database (imm_face) show that our proposed improvement leads to far better performance.
AN ILLUMINATION INVARIANT FACE RECOGNITION USING 2D DISCRETE COSINE TRANSFORM...ijcsit
Automatic face recognition performance is affected due to the head rotations and tilt, lighting intensity and
angle, facial expressions, aging and partial occlusion of face using Hats, scarves, glasses etc.In this paper,
illumination normalization of face images is done by combining 2D Discrete Cosine Transform and
Contrast Limited Adaptive Histogram Equalization. The proposed method selects certain percentage of
DCT coefficients and rest is set to 0. Then, inverse DCT is applied which is followed by logarithm
transform and CLAHE. Thesesteps create illumination invariant face image, termed as ‘DCT CLAHE’
image. The fisher face subspace method extracts features from ‘DCT CLAHE’ imageand features are
matched with cosine similarity. The proposed method is tested in AR database and performance measures
like recognition rate, Verification rate at 1% FAR and Equal Error Rate are computed. The experimental
results shows high recognition rate in AR database.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
3. Color Segmentation
Use the color information
Two approaches:
Global threshold in HSV and YCbCr space using set
of linear equations. Lot of overlap exists
(a) (b)
Clustering in (a) YCbCr and (b) V vs. H space. Red is non-face and
blue is face data
5. Second approach involves RGB vector
quantization (Linde, Buzo, Gray)
Use RGB as a 3-D vector and quantize the RGB
space for the face and non-face regions
Overlap exists in RGB space also
Sample Blue vs Green plot for face (blue)
and non-face (red) data.
8. Initial step of open and close performed to fill
holes in faces
Elongated objects removed by check on aspect
ratio and small areas discarded
9. Morphological Processing
Segmented and processed Image
consists of all skin regions (face, arms
and fists)
Need to identify centers of all objects,
including individual faces among
connected faces
Repeated EROSION is done with
specific structuring element
10. Previous state stored to identify new
regions when split occurs
Superimposed mask image with eroded
regions for estimate of centroids
11. Template Matching
Data set has 145 male and 19 female faces
Need to identify region around estimated
centroids as face or non-face
Multi-resolution was attempted. But distortion
from neighboring faces gives false values
Smaller template has better result for all face
shapes
Template used is the mean face of 50x50
pixels
Mean Face used for
template matching
12. Illumination problem identified
Top has low lighting, lower part is brighter
Left and right edges of images do not have people
2-D weighting function for correlation values
applied
2-D weighting function Sample correlation result
13. Result from template matching and thresholding.
Rejected - Red ‘x’. Detected Faces – Green ‘x’
14. EigenFace based detection
Decompose faces into set of basis images
Different methods of candidate face
extraction from image
EigenFaces
(a) (b)
Candidate face extraction (a) Conservative (b) multi-
resolution with side distortion
15. Sample result of eigenface. Red ‘+’ is from
morphological processing and green ‘O’ is from
eigenfaces
16. Minimum Distance between vector of
coefficients to that of the face dataset
was the metric.
It depends very much on spatial
similarity to trained dataset
Slight changes give incorrect results
Hence, only template matching was
used
17. Gender classification
Eigenfaces and template matching for specific face features do
not yield good results
Other features for specific females were used – the headband
Template matching was performed for it
Conservative estimate was done to prevent falsely identifying
males as a female
The headband template
26. Conclusion
RGB Vector Quantization gave excellent
segmentation
Morphological processing gave good
estimate of centroids
Template matching with illumination
correction gave near perfect results
Specific female was identified with
headband
27. Future Considerations
Edge detection to better separate the
connected faces
Preprocess the image in HSV space
before codebook comparison to improve
runtime
Improve rejection of highly correlated
non-face objects