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Computer vision old problems new solutions
The document discusses the evolution of computer vision, highlighting old problems such as scene understanding, image segmentation, and depth perception, along with their associated challenges and limitations. It details advancements in solutions, especially through the development of convolutional neural networks (CNNs) and techniques like fast R-CNN and Mask R-CNN, which improve speed and accuracy in object detection. The document also addresses the programmer's dilemma regarding image formats and emphasizes new technologies like OpenCV and Python libraries that democratize image processing.
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Computer vision old problems new solutions
- 1. Computer Vision – Old Problemsand New Solutions Gopi Krishna Nuti Vice President, MUST Research vp@must.co.in, ngopikrishna@gmail.com
- 2. Computer Vision – The (age)old problems • What should a robot do in “Scene understanding”? • Identify colours, brightness etc • Identify objects a.k.a Image Segmentation • Different things • Multiple occurrences of the same thing • Stuff other than things • Distance of things and stuff • Relative and absolute
- 3. Colour and Brightness Colour spaces •Grayscale, RGB, CMY, • Transparency/Opacity using a fourth attribute Limitations • Does not represent all colours in nature • colour perception highly susceptible to lighting changes. New Solutions • Colour spaces have been expanded greatly. • With micro and macro level differences, ~250 colour spaces are in vogue • HSV, HSL/HSI, YUV, YPbPr, YCbCr etc
- 4. Old Problem – Image Segmentation Panoptic Segmentation– Not a technique. A metric
- 5. Old Problem – Image Segmentation • Imageis an matrix of numbers. • How to identify the edges of each object • How to recognize the object correctly • Differentiate between “things” (foreground) and “stuff ” (background)
- 6. Image Segmentation – Old Solutions Solution FamilyAlgorithm Drawbacks Thresholding • Otsu thresholding • Adaptive local thresholding • Mean • Gaussian For reasonably simple scenarios only Edges and Corners • Canny edges, Sobel Hough, Laplace algorithms • Harris Corner detection • Convolution of kernels Unsuitable for noisy/blurry images Region Growing Watershed • Relatively strong at detecting overlapping/touching objects Super Pixels • SLCI Algorithm • Susceptible to noise • Steep increase in algorithmic complexit Clustering • K-means • Fuzzy C-Means (FCM) • Expectation Maximization (EM) • Relies on low level features like colour etc. • Poor performance on complicated images Clustering • Image Pyramid • Carefully controlled environments only • Cannot handle non-affine transformation like rotation, reflection etc. • Occlusions are a big no-no • Compute intensive
- 7. Image Segmentation – Convolutional Neural Networks • Specialized kindof neural networks • Process data in known grid-like spatial structures • Comprised of large number of layers like convolution, pooling and Fully connected layers • Usually, very very deep. i.e. lots of layers and lots of weight parameters • Non linear Activation Functions are mandatory for learning complex features
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- 9. Evolution of CNN Classifiers 2014 • Regions withCNN Features 2015 • Fast R-CNN • Faster R-CNN • Inception V3 2016 • YOLO • SSD • UberNet 2017 • Mask R- CNN • Pixel wise Instance Segmentation
- 10. Some Salient points Regions withCNN FeaturesR-CNN • Uses Selective Search • Significantly reduced the search space to ~2000 region proposal • Very Slow and very complicated Designed to solve the problems with R-CNNFast R-CNN • Region Of Interest is treated as a pooling layer • Jointly trains feature extractor, classifier and bounding box regression into a single model • Almost 25 time faster than R-CNN Replace Selective search with region proposal networkFaster R-CNN • 10 times faster than Fast R-CNN You Only Look OnceYOLO • Detection is considered as a regression problem • Extremely fast but less accurate. Struggles with small objects that appear in groups Single Shot Multi box detectorSSD • Faster than YOLO and more accurate as well. Extension of Faster R-CNNMask R-CNN • Predicts the object masks as well as bounding box • Impressive results
- 11. Old Problem - Depth Perception Normal visionand depth perception expectation Relative depth Optical illusion based on depth Picture of a picture. All pixels have same depth
- 12. Old Solutions - Depth Perception • Stereocameras spaced at a fixed distance apart capture the same image. • Remember trigonometry? • Algorithm Families • Triangulation • Interferometry • Time of Flight • Many Limitations • Cost • Complexity • Controlled environments only
- 13. New Solutions - Depth Perception • Furiousresearch in progress • Single camera moving between two fixed positions • Monocular Depth perception • Some interesting proposals • Train NN with depth information and semantically segmented image • Use the models for predicting depth in new images
- 14. Old Problem – Programmer’sDilemma
- 15. Old Problem - Programmers Dilemma •Which image format should I use? • Which image file format should I code for? Do I have to learn reading and writing image files? • Matlab is expensive
- 16. New Solution - OpenCV, Python, PILLOWetc • OpenCV • Democratized image processing • A large number of functionalities provided as APIs • Impressive Python bindings and native support for C, Java • Python • PILLOW and many other libraries for reading images • Vectorization and Numpy Arrays
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- 18. Neural Networks • Data hungry.Lots and lots of training data. • Resource hungry and compute intensive. • Overfitting, Underfitting, Stochasticity • Black box
- 19. Some solutions • TransferLearning to reduce training time • Hyper parameter tuning • Hardware based solutions for improving performance • On-going research for explainability • On-going research for reducing the training data requirement 3rd generation neural networks
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