But first, let’s address the question, “What is computer vision?” In simple terms, computer vision trains the computer to visualize the world just like we humans do. Computer vision techniques are developed to enable computers to “see” and draw analysis from digital images or streaming videos. The main goal of computer vision problems is to use the analysis from the digital source data to convert it into something about the world.
Computer vision uses specialized methods and general recognition algorithms, making it the subfield of artificial intelligence and machine learning. Here, when we talk about drawing analysis from the digital image, computer vision focuses on analyzing descriptions from the image, which can be text, object, or even a three-dimensional model. In short, computer vision is a method used to reproduce the capability of human vision.
As studied earlier, computer networks are one of the most popular and well-researched automation topics over the last many years. But along with advantages and uses, computer vision has its challenges in the department of modern applications, which deep neural networks can address quickly and efficiently.
With the soaring demand for computing power and storage, it is challenging to deploy deep neural network applications. Consequently, while implementing the neural network model for computer vision, a lot of effort and work is put in to increase its precision and decrease the complexity of the model.
For example, to reduce the complexity of networks and increase the result accuracy, we can use a singular value decomposition matrix to obtain the low-rank approximation.
After the model training for computer vision, it is crucial to eliminate the irrelevant neuron connections by performing several filtrations of fine-tuning. Therefore, as a result, it will increase the difficulty of the system to access the memory and cache.
Sometimes, we also have to design a unique collaborative database as a backup. In comparison to that, filter-level pruning helps to directly refine the current database and determine the filter’s importance in the process.
The data outcome of the system consists of 32 bits floating point precision. But the engineers have discovered that using the half-precision floating points, taking up to 16 bits, does not affect the model’s performance. As the final solution, the range of data is either two or three values as 0/1 or 0/1/-1, respectively.
The computation of the model was effectively increased using this reduction of bits, but the challenge remained of training the model for two or three network value core issues. As we can use two or three floating-point values, the researcher suggested using three floating-point scales to increase the representation of the network.
It is difficult for the system to identify the image’s class precisely when it comes to image classification. For example, if we want to determine the exact type of a bird, it generally classifies it into a minimal class. It cannot precisely identify the exact difference between two bird species with a slight difference. But, with fine-grained image classification, the accuracy of image processing increases.
Fine-grained image classification uses the step-by-step approach and understanding the different areas of the image, for example, features of the bird, and then analyzing those features to classify the image completely. Using this, the precision of the system increases but the challenge of handling the huge database increases. Also, it is difficult to tag the location information of the image pixels manually. But in comparison to the standard image classification process, the advantage of using fine-grained classification is that the model is supervised by using image notes without additional training.
Bilinear CNN helps compute the final output of the complex descriptors and find the relation between their dimensions as dimensions of all descriptors analyze different semantic features for various convolution channels. However, using bilinear operation enables us to find the link between different semantic elements of the input image.
When the system is given a typical image and an image with a fixed style, the style transformation will retain the original contents of the image along with transforming the image into that fixed style. The texture synthesis process creates a large image consisting of the same texture.
The fundamentals behind texture synthesis and style transformation are feature inversion. As studied, the style transformation will transform the image into a specific style similar to the image given using user iteration with a middle layer feature. Using feature inversion, we can get the idea of the information of an image in the middle layer feature.
The feature inversion is performed over the texture image, and using it, the gram matrix of each layer of the texture image is created just like the gram matrix of each feature in the image.
The low-layer features will be used to analyze the detailed information of the image. In contrast, the high layer features will examine the features across the larger background of the image.
We can process the style transformation by creating an image that resembles the original image or changing the style of the image that matches the specified style.
Therefore, during the process, the image’s content is taken care of by activating the value of neurons in the neural network model of computer vision. At the same time, the gram matrix superimposes the style of the image.
The challenge faced by the traditional style transformation process is that it takes multiple iterations to create the style-transformed image, as suggested. But using the algorithm which trains the neural network to generate the style transformed image directly is the best solution to the above problem.
The direct style transformation requires only one iteration after the training of the model ends. Also, calculating instance normalization and batch normalization is carried out on the batch to identify the mean and variance in the sample normalization.
The problem faced with generating the direct style transformation process is that the model has to be trained manually for each style. We can improve this process by sharing the style transformation network with different styles containing some similarities.
It changes the normalization of the style transformation network. So, there are numerous groups with the translation parameter, each corresponding to different styles, enabling us to get multiple styles transformed images from a single iteration process.
There is a vast increase in the use cases of face verification/recognition systems all over the globe. The face verification system takes two images as input. It analyzes whether the images are the same or not, whereas the face recognition system helps to identify who the person is in the given image. Generally, for the face verification/recognition system, carry out three basic steps:
The major challenge for carrying out face verification/recognition is that learning is executed on small samples. Therefore, as default settings, the system’s database will contain only one image of each person, known as one-shot learning.
It is the first face verification/recognition model to apply deep neural networks in the system. DeepFace verification/recognition model uses the non-shared parameter of networks because, as we all know, human faces have different features like nose, eyes, etc.
Therefore, the use of shared parameters will be inapplicable to verify or identify human faces. Hence, the DeepFace model uses non-shared parameters, especially to identify similar features of two images in the face verification process.
FaceNet is a face recognition model developed by Google to extract the high-resolution features from human faces, called face embeddings, which can be widely used to train a face verification system. FaceNet models automatically learn by mapping from face images to compact Euclidean space where the distance is directly proportional to a measure of face similarity.
Here the three-factor input is assumed where the distance between the positive sample is smaller than the distance between the negative sample by a certain amount where the inputs are not random; otherwise, the network model would be incapable of learning itself. Therefore, selecting three elements that specify the given property in the network for an optimal solution is challenging.
Liveness detection helps determine whether the facial verification/recognition image has come from the real/live person or a photograph. Any facial verification/recognition system must take measures to avoid crimes and misuse of the given authority.
Currently, there are some popular methods in the industry to prevent such security challenges as facial expressions, texture information, blinking eye, etc., to complete the facial verification/recognition system.
When the system is provided with an image with specific features, searching that image in the system database is called Image Searching and Retrieval. But it is challenging to create an image searching algorithm that can ignore the slight difference between angles, lightning, and background of two images.
As studied earlier, image search is the process of fetching the image from the system’s database. The classic image searching process follows three steps for retrieval of the image from the database, which are:
The challenge faced by the classic image search process is that the performance and representation of the image after the search engine algorithm are reduced.
The image retrieval process without any supervised outside information is called an unsupervised image search process. Here we use the pre-trained model ImageNet, which has the set of features to analyze the representation of the image.
Here, the pre-trained model ImageNet connects it with the system database, which is already trained, unlike the unsupervised image search. Therefore, the process analyzes the image using the connection, and the system dataset is used to optimize the model for better results.
The process of analyzing the movement of the target in the video is called object tracking. Generally, the process begins in the first frame of the video, where a box around it marks the initial target. Then the object tracking model assumes where the target will get in the next frame of the video.
The limitation to object tracking is that we don’t know where the target will be ahead of time. Hence, enough training is to be provided to the data before the task.
The usage of health networks is just similar to a face verification system. The health network consists of two input images where the first image is within the target box, and the other is the candidate image region. As an output, the degree of similarity between the images is analyzed.
In the health network, it is not necessary to visit all the candidates in the different frames. Instead, we can use a convolution network and traverse each image only once. The most important advantage of the model is that the methods based on this network are high-speed and can process any image irrespective of its size.
CFNet is used to elevate the tracking performance of the weighted network along with the health network training model and some online filter templates. It uses Fourier transformation after the filters train the model to identify the difference between the image regions and the background regions.
Apart from these, other significant problems are not covered in detail as they are self-explanatory. Some of those problems are:
Originally published here