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DEEP LEARNING
Computer Vision
Annisa Darmawahyuni Machine Learning Study Jams, 2024
DEEP LEARNING BASIC
“ARTIFICIAL NEURAL NETWORKS”
ARTIFICIAL NEURAL NETWORKS
MACHINE VS DEEP LEARNING
Annisa Darmawahyuni
“Deep learning allows computational models of multiple
processing layers to learn and represent data with multiple
levels of abstraction mimicking how the brain perceives and
understands multimodal information, thus implicitly capturing intricate
structures of large-scale data”
Annisa Darmawahyuni
(a) Face Detection
(b) Object Instance Segmentation
(c) Structure from motion (3D)
(d) Stereo Matching (3D)
COMPUTER
VISION
Annisa Darmawahyuni
Computer vision is a field of artificial intelligence (AI) that enables
computers and systems to derive meaningful information from digital
images, videos and other visual inputs — and take actions or make
recommendations based on that information. If AI enables computers to
think, computer vision enables them to see, observe and understand.
COMPUTER
VISION
Ti
Annisa Darmawahyuni
Timeline of topic research in computer vision
Ti
Annisa Darmawahyuni
Annisa Darmawahyuni
Annisa Darmawahyuni
COMPUTER VISION
MACHINE LEARNING DEEP LEARNING
Haar-like wavelet feature and integral graph
method
K-means, Naive Bayes classifier, Decision
Tree, Boosting, Random Forest, Haar
Classifier, Expectation–Maximization (EM), K-
Nearest Neighbor (KNN), and Support Vector
Machine (SVM
Convolutional Neural Networks (CNNs),
Restricted Boltzmann Machines (RBMs),
Autoencoders, Sparse Coding
Annisa Darmawahyuni
CNN FOR
COMPUTER VISION
Annisa Darmawahyuni
OBJECT DETECTION
Object detection is the process of detecting instances of semantic objects of a certain class (such as humans,
airplanes, or birds) in digital images and video.
Ground truth Bounding Box with region approach Bounding Box with region and semantic
segmentation approach
Annisa Darmawahyuni
OBJECT DETECTION
You can choose from two key approaches to get started with object detection using deep learning:
Create and train a custom object detector.
To train a custom object detector from scratch, you need to design a network architecture to learn
the features for the objects of interest. You also need to compile a very large set of labeled data to
train the CNN. The results of a custom object detector can be remarkable. That said, you need to
manually set up the layers and weights in the CNN, which requires a lot of time and training data.
Use a pretrained object detector.
Many object detection workflows using deep learning leverage transfer learning, an approach that
enables you to start with a pretrained network and then fine-tune it for your application. This
method can provide faster results because the object detectors have already been trained on
thousands, or even millions, of images.
Annisa Darmawahyuni
OBJECT DETECTION
Annisa Darmawahyuni
OBJECT DETECTION
SEGMENTATION
Annisa Darmawahyuni
SEMANTIC SEGMENTATION
Semantic Segmentation is a deep learning algorithm that associates a label or category with every pixel in an
image. It is used to recognize a collection of pixels that form distinct categories
A simple example of semantic segmentation is separating the images into two classes. For example, in Figure 1, an image showing a person
at the beach is paired with a version showing the image's pixels segmented into two separate classes: person and background.
Annisa Darmawahyuni
HOW DOES SEMANTIC SEGMENTATION
DIFFER FROM OBJECT DETECTION?
Semantic segmentation can be a useful alternative to object detection because it allows the object of interest to span
multiple areas in the image at the pixel level. This technique cleanly detects objects that are irregularly shaped, in
contrast to object detection, where objects must fit within a bounding box (Figure 2)
Figure 2. Object detection, showing bounding boxes to identify objects.
Annisa Darmawahyuni
SEMANTIC SEGMENTATION
Annisa Darmawahyuni
SEMANTIC
SEGMENTATION
The process of training a semantic segmentation network to
classify images follows these steps:
Analyze a collection of pixel-labeled images.
Create a semantic segmentation network.
Train the network to classify images into pixel categories.
Assess the accuracy of the network
Annisa Darmawahyuni
SEMANTIC SEGMENTATION
Highway scene showing color image (left) and corresponding labeled pixels (right)
Annisa Darmawahyuni
DATASET FOR COMPUTER VISION
Grayscale Images. The most used grayscale images dataset is MNIST
(https://www.kaggle.com/datasets/hojjatk/mnist-dataset) and its variations, that is, NIST and perturbed
NIST. The application scenario is the recognition of handwritten digits.
RGB Natural Images. Caltech RGB image datasets (https://euclid.caltech.edu/image/euclid20231107b-
ngc-6822), CIFAR datasets (https://www.cs.toronto.edu/~kriz/cifar.html) consist of thousands of 32 × 32
color images in various classes.
Hyperspectral Images. SCIEN hyperspectral image data and AVIRIS sensor based datasets, for example,
contain hyperspectral images.
Facial Characteristics Images. Adience benchmark dataset
Medical Images. Chest X-ray dataset (https://www.kaggle.com/datasets/paultimothymooney/chest-xray-
pneumonia) comprises 112120 frontal-view X-ray images of 30805 unique patients.
Video Streams. The WR datasets can be used for video-based activity recognition in assembly lines.
YouTube-8M is a dataset of 8 million YouTube video URLs, along with video-level labels from a diverse set
of 4800 Knowledge Graph entities.
Annisa Darmawahyuni
PARAMETER VS HYPERPARAMETER
Annisa Darmawahyuni
HYPERPARAMETER TUNING (DL)
Learning rate (LR). If the learning rate (LR) is too small, overfitting can occur. Large learning rates help to
regularize the training but if the learning rate is too large, the training will diverge.
Number of hidden layers.
Number of nodes/neurons per layer.
Optimizer
Batch Size
Epochs
Artikel Ilmiah Computer Vision Deep Learning
Intelligent System Research Group
https://docs.google.com/spreadsheets/d/13MLJnecd32B3H-f342M-
Uoqd_y5wRVgGDK1aT-bQg3w/edit#gid=0
annisadarmawahyuni@unsri.ac.id
riset.annisadarmawahyuni@gmail.com

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Deep Learning For Computer Vision- Day 3 Study Jams GDSC Unsri.pptx

  • 1. DEEP LEARNING Computer Vision Annisa Darmawahyuni Machine Learning Study Jams, 2024
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  • 7. MACHINE VS DEEP LEARNING Annisa Darmawahyuni
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  • 10. “Deep learning allows computational models of multiple processing layers to learn and represent data with multiple levels of abstraction mimicking how the brain perceives and understands multimodal information, thus implicitly capturing intricate structures of large-scale data”
  • 11. Annisa Darmawahyuni (a) Face Detection (b) Object Instance Segmentation (c) Structure from motion (3D) (d) Stereo Matching (3D) COMPUTER VISION
  • 12. Annisa Darmawahyuni Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe and understand. COMPUTER VISION
  • 13. Ti Annisa Darmawahyuni Timeline of topic research in computer vision
  • 16. Annisa Darmawahyuni COMPUTER VISION MACHINE LEARNING DEEP LEARNING Haar-like wavelet feature and integral graph method K-means, Naive Bayes classifier, Decision Tree, Boosting, Random Forest, Haar Classifier, Expectation–Maximization (EM), K- Nearest Neighbor (KNN), and Support Vector Machine (SVM Convolutional Neural Networks (CNNs), Restricted Boltzmann Machines (RBMs), Autoencoders, Sparse Coding
  • 18. Annisa Darmawahyuni OBJECT DETECTION Object detection is the process of detecting instances of semantic objects of a certain class (such as humans, airplanes, or birds) in digital images and video. Ground truth Bounding Box with region approach Bounding Box with region and semantic segmentation approach
  • 19. Annisa Darmawahyuni OBJECT DETECTION You can choose from two key approaches to get started with object detection using deep learning: Create and train a custom object detector. To train a custom object detector from scratch, you need to design a network architecture to learn the features for the objects of interest. You also need to compile a very large set of labeled data to train the CNN. The results of a custom object detector can be remarkable. That said, you need to manually set up the layers and weights in the CNN, which requires a lot of time and training data. Use a pretrained object detector. Many object detection workflows using deep learning leverage transfer learning, an approach that enables you to start with a pretrained network and then fine-tune it for your application. This method can provide faster results because the object detectors have already been trained on thousands, or even millions, of images.
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  • 26. Annisa Darmawahyuni SEMANTIC SEGMENTATION Semantic Segmentation is a deep learning algorithm that associates a label or category with every pixel in an image. It is used to recognize a collection of pixels that form distinct categories A simple example of semantic segmentation is separating the images into two classes. For example, in Figure 1, an image showing a person at the beach is paired with a version showing the image's pixels segmented into two separate classes: person and background.
  • 27. Annisa Darmawahyuni HOW DOES SEMANTIC SEGMENTATION DIFFER FROM OBJECT DETECTION? Semantic segmentation can be a useful alternative to object detection because it allows the object of interest to span multiple areas in the image at the pixel level. This technique cleanly detects objects that are irregularly shaped, in contrast to object detection, where objects must fit within a bounding box (Figure 2) Figure 2. Object detection, showing bounding boxes to identify objects.
  • 29. Annisa Darmawahyuni SEMANTIC SEGMENTATION The process of training a semantic segmentation network to classify images follows these steps: Analyze a collection of pixel-labeled images. Create a semantic segmentation network. Train the network to classify images into pixel categories. Assess the accuracy of the network
  • 30. Annisa Darmawahyuni SEMANTIC SEGMENTATION Highway scene showing color image (left) and corresponding labeled pixels (right)
  • 31. Annisa Darmawahyuni DATASET FOR COMPUTER VISION Grayscale Images. The most used grayscale images dataset is MNIST (https://www.kaggle.com/datasets/hojjatk/mnist-dataset) and its variations, that is, NIST and perturbed NIST. The application scenario is the recognition of handwritten digits. RGB Natural Images. Caltech RGB image datasets (https://euclid.caltech.edu/image/euclid20231107b- ngc-6822), CIFAR datasets (https://www.cs.toronto.edu/~kriz/cifar.html) consist of thousands of 32 × 32 color images in various classes. Hyperspectral Images. SCIEN hyperspectral image data and AVIRIS sensor based datasets, for example, contain hyperspectral images. Facial Characteristics Images. Adience benchmark dataset Medical Images. Chest X-ray dataset (https://www.kaggle.com/datasets/paultimothymooney/chest-xray- pneumonia) comprises 112120 frontal-view X-ray images of 30805 unique patients. Video Streams. The WR datasets can be used for video-based activity recognition in assembly lines. YouTube-8M is a dataset of 8 million YouTube video URLs, along with video-level labels from a diverse set of 4800 Knowledge Graph entities.
  • 33. Annisa Darmawahyuni HYPERPARAMETER TUNING (DL) Learning rate (LR). If the learning rate (LR) is too small, overfitting can occur. Large learning rates help to regularize the training but if the learning rate is too large, the training will diverge. Number of hidden layers. Number of nodes/neurons per layer. Optimizer Batch Size Epochs Artikel Ilmiah Computer Vision Deep Learning Intelligent System Research Group https://docs.google.com/spreadsheets/d/13MLJnecd32B3H-f342M- Uoqd_y5wRVgGDK1aT-bQg3w/edit#gid=0