Deep learning neural networks are taking over statistical methods in computer vision. Importantly, deep learning methods provide magnificent results on certain problems. However, computer vision still faces many challenging issues.
Bonus Read: 5 Best Ai Image to Text Conversion Tools
The most interesting part is the performance of deep learning models on benchmark problems as a single model can decipher images and perform vision tasks, eliminating the requirement of specialized and hand-crafted methods.
In this post, you will read fascinating computer vision tasks where deep learning methods are advancing.
Using deep learning to solve computer vision problems:
- Image Classification
- Image Classification With Localization
- Object Detection
- Object Segmentation
- Image Style Transfer
- Image Colorization
- Image Reconstruction
- Image Superresolution
- Image Synthesis
Applications for image classification are employed in various fields, including medical imaging, satellite image object identification, traffic control systems, brake light detection, machine vision, and more.
Image Classification
In image classification, an entire image or photograph is labeled.
A much broader set of tasks related to classifying the content of images may be included in this problem, also known as “object classification” and perhaps more generally as “image recognition.”
Some image classification examples include:
● An x-ray is classified as cancerous or not (binary classification).
● Classifying a handwritten digit (multiclass classification).
● Assigning a name to a photograph of a face (multiclass classification).
There are several benchmark problems in image classification, but MNIST is a popular one.
Street View House Numbers (SVHN) is a real-world dataset that classifies photos of digits.
For state-of-the-art results and relevant papers on these and other image classification tasks, see:
● What is the class of this image?
Photographs of objects are often used in image classification tasks. In these datasets, photographs are classified into 10 and 100 categories, respectively, according to CIFAR-10 and CIFAR-100 datasets.
Large Scale Visual Recognition Challenge (ILSVRC) invites teams to compete for best performance in a wide range of computer vision tasks based on ImageNet data. It is particularly important to note that, as a result of early papers on the image classification task, many advances have been made in image classification.
Image Classification With Localization
In image classification with localization, a class label is assigned to an image, and a bounding box shows where the object is in the image (drawing a box around the object).
This is a more challenging version of image classification.
Some examples of image classification with localization include:
● Drawing a box around the cancerous region of an x-ray after labeling it as cancer or not.
● In each scene, classify photographs of animals by drawing a box around the animal.
A classical set of PASCAL VOC datasets is used for image classification with localization (e.g., VOC 2012). Over the years, these datasets have been used in computer vision challenges.
In the image, bounding boxes may be added around multiple examples of the same object. Consequently, object detection may sometimes be used to describe this task.
150,000 photos with 1,000 categories of objects are included in the ILSVRC2016 Dataset for image classification with localization.
Object Detection
Although an image can contain multiple objects that require localization and classification, object detection is the process of classifying an image with localization.
Since there are multiple objects in the image of different types, this is a more challenging task than simple image classification.
Object detection often relies on techniques developed for image classification with localization.
Some examples of object detection include:
● Drawing a bounding box and labeling each object in a street scene.
● Drawing a bounding box and labeling each object in an indoor photograph.
● Drawing a bounding box and labeling each object in a landscape.
PASCAL Visual Object Classes datasets, or PASCAL VOC for short (e.g., VOC 2012), are common datasets that detect objects.
The Microsoft Common Objects in Context Dataset often called MS COCO, is another dataset useful for multiple computer vision tasks.
Object Segmentation
A line is drawn around each detected object in an image during object or semantic segmentation. Slicing up an image into segments is the problem of image segmentation.
Segmenting objects is also known as object detection.
Segmentation of an image identifies the specific pixels in the image that belong to the object, unlike object detection, which uses bounding boxes to identify objects. It is like a fine-grained localization.
As a general concept, ” image segmentation” refers to categorizing all pixels in an image.
Both the VOC 2012 and MS COCO datasets can be used for object segmentation.
Another popular object segmentation dataset, the KITTI Vision Benchmark Suite, provides images of streets for training autonomous vehicle models.
Style Transfer
Transferring style from one or more images to another is called style transfer or neural style transfer.
Essentially, this task can be seen as an objectively evaluable sort of photo filter or transform.
An example is taking a new photograph and applying the style of a famous artwork (e.g., by Pablo Picasso or Vincent van Gogh).
Public domain artworks and standard computer vision datasets are often used as data sets.
Image Colorization
Converting a grayscale image into a full-color image is known as image colorization or neural colorization.
An objective evaluation of this task may not be possible because it is a form of filter or transformation.
Among the examples are colorizing old photographs and movies that were previously black and white.
Models are often trained to colorize grayscale versions of existing photo datasets.o
Image Reconstruction
Reconstruction and image inpainting are techniques for fixing damaged or missing parts of an image.
An objective evaluation of this task may not be possible because it is a form of filter or transformation.
For instance, you might re-create old, damaged black-and-white photos or films (e.g., photo restoration).
It is often the case that models are required to learn how to repair corrupted versions of photos in datasets that use existing camera datasets.
Image Superresolution
The superresolution process recreates images with a higher resolution and detail than original images. Restoration of images, and ideas require models developed for superresolution as they solve such problems.
Down-scaled versions of photos are created for those datasets, as superresolution versions must be created for umadeisting photo datasets.
Image Synthesis
Creating new images or modifying existing images is the goal of image synthesis.
In this rapidly advancing field, there is a wide range of applications.
It could involve minor adjustments to images and videos (e.g., image-to-image translations), such as:
● Changing the style of an object in a scene.
● Adding an object to a scene.
● Adding a face to a scene.
Additionally, it could entail creating own graphics, like:
● Generating faces.
● Generating bathrooms.
● Generating clothes.
