Computer Vision » The Three R’s of Computer Vision

 

What is Computer Vision?

Two definitions of computer vision Computer vision can be defined as a scientific field that extracts information out of digital images. The type of information gained from an image can vary from identification, space measurements for navigation, or augmented reality applications. Another way to define computer vision is through its applications. Computer vision is building algorithms that can understand the content of images and use it for other applications. Computer vision brings together a large set of disciplines.

Neuroscience can help computer vision by first understanding human vision, as we will see later on. Computer vision can be seen as a part of computer science and algorithm theory or machine learning are essential for developing computer vision algorithms. Computer vision seeks to generate intelligent and useful descriptions of visual scenes and sequences, and of the objects that populate them, by performing operations on the signals received from video cameras. Computer vision is the field of study encompassing how computer systems view, witness, and comprehend digital data imagery and video footage.

Computer vision spans all of the complex tasks performed by biological vision processes. These include 'seeing' or sensing visual stimulus, comprehending exactly what has been seen and filtering this complex information into a format used for other processes. This interdisciplinary field automates the key elements of human vision systems using sensors, smart computers, and machine learning algorithms. Computer vision is the technical theory underlying artificial intelligence systems' capability to view - and understand - their surroundings.

Applications of Computer Vision

Numerous examples of computer vision have been practically applied because - by its pure theory - it can be adopted, providing a computer vision system that can 'see' and 'comprehend' its surroundings. Below are a few key examples of computer vision systems:

Autonomous Vehicles: Self-driving automobiles use CV systems to gather information regarding their surroundings and interpret that data to determine their next actions and behavior.

Robotic Applications: Manufacturing robotic machines using CV, 'view' and 'comprehend' their surroundings to perform their scheduled tasks. In manufacturing, such systems inspect assembly items to determine faults and tolerance limits - simply by 'looking' at them as they traverse the production line. 

Image Search and Object Recognition: Applications use CV data vision theory to identify specific objects within digital images, search through catalogs of product images, and extract information from photos.

Facial Recognition: Businesses and Government departments use facial recognition technology (that have adopted CV) to 'see' precisely what an individual is trying to gain access to.

Successful integration and interdisciplinary processes are keys to thriving modern science and its application within the industry. One such interdisciplinary approach has been the recent endeavors to combine the fields of computer vision and natural language processing.

These technical domains are among the most popular - and active - machine learning research sciences that are currently prospering. Nonetheless, until quite recently, they have been administered as separate technical entities without discovering the key benefits from them both. It has only been recently, with the expansion of digital multimedia, that scientists, and researchers, have begun exploring the possibilities of applying both techniques to accomplish one promising result.

What is Natural Language Processing?

Natural language processing is the capability of a 'smart' computer system to understand human language - as it is both written and spoken. This is commonly referred to as natural language. Natural language processing is a technical component or subset of artificial intelligence. Natural language processing has existed for well over fifty years, and the technology has its origins in linguistics or the study of human language. It has an assortment of real-world applications within a number of industries and fields, including intelligent search engines, advanced medical research, and business processing intelligence.

How Does Natural Language Processing Work?

Natural language processing facilitates and encourages computers to understand natural language, as we humans can and do. Regardless of whether the language is written or spoken, natural language processing uses artificial intelligence to receive real-life input, process it accordingly, and provide the indicative meaning of the results in a manner that a computer can readily comprehend.

Just as we humans have various natural senses, such as eyes to see with or ears to hear; computers support program instructions to read language text and microphones to collect and analyze audio. Similar to how humans use their brains to process input, computers have a program instruction set to process their inputs and information. After processing occurs, this input is transformed into code that only the computer system can interpret.

There are two main stages to the natural language processing process: 

• Data preprocessing, and 
• Algorithm development.

The data preprocessing stage involves preparing or 'cleaning' the text data into a specific format for computer devices to analyze. The preprocessing arranges the data into a workable format and highlights features within the text. This enables a smooth transition to the next step - the algorithm development stage - which works with that input data without any initial data errors occurring.

Challenges of Natural Language Processing

There are several challenges that natural language processing supplies researchers and scientists with, and they predominantly relate to the ever-maturing and evolving natural language process itself.

Precision, and sometimes the lack of it: Computers have traditionally required humans to communicate with them using a specific language - or a programming language. These programming languages are precise, without ambiguity, and highly structured. However, human speech is not always a precise form of communication; it can be frequently imprecise. The linguistic structure depends on numerous complex variables, including slang, provincial dialects used, and the social context of the spoken language.

Voice tone and inflection: As previously stated, natural language processing is an iterative process striving for perfection. For example, semantic analysis is still a key challenge. Other complications involve the abstract use of language and how this is problematic for such systems to comprehend accurately. Natural language processing cannot readily interpret sarcasm. Also, sentence structure can change meaning depending on which syllable or word the speaker emphasizes or stresses. Natural language algorithms may miss the subtle but important tonal changes within a speaker's voice with speech recognition. Compounding this issue is that the tone and inflection of speech will vary between diverse accents, providing challenges for an algorithm to parse successfully.

The evolution and use of language: Natural language processing is challenged by the reality that human languages - and how different societies use them - are continually changing. While acknowledging specific rules exist for writing and speaking a language, they are subject to adaptation over time. Rigid computational directions and guidelines that work presently may become obsolete as the attributes of real-world languages change.

Natural language processing tasks are deemed more technically diverse when compared to computer vision procedures. This diversification ranges from variable syntax identification, morphology and segmentation capabilities, and semantics to study abstract meaning. Complex tasks within natural language processing include direct machine translation, dialogue interface learning, digital information extraction, and prompt key summarization. However, computer vision is advancing more rapidly in comparison with natural language processing. And this is primarily due to the massive interest in computer vision - and the financial support provided by large tech companies such as Meta and Google. 

Future of Integration of Natural Language Processing and Computer Vision

Once completely integrated and combined, these two technologies can resolve numerous challenges that are present within multiple fields, including

Designing: Within the area of home design, designer clothes, jewelry making, etc., customer systems can understand verbal or written requirements and thereby automatically convert these instructions to digital images for enhanced visualization. 

Describing Medical Images: computer vision systems can be trained to identify more modest human ailments and use digital imagery in finer detail than human medical specialists.

Converting Sign Language: to speech or written text to assist the deaf and hard of hearing individuals in interacting with their surroundings. This enhanced capability can ensure their better integration within society.

Surrounding Cognition: Constructing an intelligent system that 'sees' its surroundings and delivers a (recorded) spoken narrative. This outcome will be of use for visually impaired individuals.

Converting Words to Images: Producing intelligent systems that convert spoken content to a digital image may assist people who do not talk and hear.

Computer Vision and its Relation to Natural Language Processing / The three R’s of computer vision 

The combination of natural language processing and computer vision involves three key interrelated processes: recognition, reconstruction, and reorganization. 

Recognition: This process involves assigning digital labels to objects within the image. Examples of recognition are handwriting or facial recognition for 2D objects, and 3D assignments handle challenges such as moving object recognition which helps in automatic robotic manipulation. 

Reconstruction: This process refers to 3D scene rendering given inputs from particular visual images by incorporating multiple viewpoints, digital shading, and sensory depth data. The outcome results in a 3D digital model that is then used for further processing.

Reorganization: This process refers to raw pixel segmentation into data groups that represent the design of a pre-determined configuration. Low-level vision tasks include corner detection, edges, and contours; while high-level tasks involve semantic segmentation, which can partly overlap with recognition processes.

Recognition helps reorganization

Object proposal generation methods such as those described earlier typically rely on the coherence of color and texture to segment the image out into likely object candidates. However, such cues can often make mistakes. For instance, the boundary between the dog in Fig. 6 and the wall is barely perceptible, while the meaningless contour between the dog’s face and its torso is sharp. However, once an object detection approach such as R-CNN detects the dog, we can bring to bear our knowledge

Recognition helps reconstruction Consider Fig. 10. As humans, we can easily perceive the 3D shape of the shown object, even though we might never have seen this particular object instance. We can do this because we don’t experience this image tabula rasa, but in the context of our “remembrance of things past”. We can recognize that this is the image of a car as well as estimate the car’s 3D pose. Previously seen cars enable us to develop a notion of the 3D shape of cars, which we can project to this particular instance using 

Reconstruction helps reorganization RGB-D sensors like the Microsoft Kinect, provide a depth image in addition to the RGB image. We use this additional depth information as ‘reconstruction’ input and study the reorganization problems. In particular we study the problem of contour detection and region proposal generation. 

Reconstruction helps recognition We can also use the depth image from a Kinect sensor to aid recognition. More specifically, we study how reconstruction input in the form of a depth image from a RGB-D sensor can be used to aid performance for the task of object detection. The set of categories that we study here included indoor furniture categories like chairs, beds, sofas, and tables frame this problem as a feature learning problem and use a convolutional neural.

Reorganization helps reconstruction the leftmost column depicts a video scene that contains multiple moving objects. We want to reconstruct their spatio-temporal 3D shapes. Extensive literature exists on reconstructing static scenes from monocular uncalibrated videos, a task also known as rigid Structure-from-Motion (SfM). Some works employ scaled orthographic cameras with rank shape priors, such as the seminal factorization work of , while others assume perspective cameras and make use of epipolar. 

Reorganization helps recognition As noted earlier, the dominant approach to object detection has been based on slidingwindow detectors. This approach goes back (at least) to early face detectors, and continued with HOG-based pedestrian detection , and part-based generic object detection . Straightforward application requires all objects to share a common aspect ratio.

Basics of Image Processing

A digital image is a visual representation of a numerical array that meassures a physical phenomenon. Therefore, a digital image can be seen as an array of numbers, on which mathematical operations can be done. The array is not imposed to be of two dimensions: it can be of many dimensions, depending of the type of image acquisition.

The acquisition devices are never perfect and introduce several modifications of the image, such as subsampling, quantization, noise, etc. Then we see how to display an image (i.e. an array of number) into a visual representation, especially the link between the numbers and the colors. At last, we introduce some very simple processing’s with arithmetic operations.

There are many different image formats used for storing and transmitting images in compressed form, since raw images are large data structures that contain much redundancy (e.g. correlations between nearby pixels) and thus are highly compressible. 

Different formats are specialized for compressibility, manipulability, or the properties of printers and browsers. Some examples: 

• .jpeg - ideal for variable compression of continuous color images, with a “quality factor” (typically 75) that can be specified. Useful range of DCT compression goes from 100:1 (“lossy”) to about 10:1 (almost lossless). 

• .mpeg - a stream-oriented, compressive encoding scheme used mainly for video (but also multimedia). Individual image frames are .jpeg compressed, but an equal amount of redundancy is removed temporally by inter-frame predictive coding and interpolation. 

• .gif - ideal for sparse binarized images. Only 8-bit colour. Very compressive and favoured for web-browsers and other bandwidth-limited media. 

• .tiff - A complex umbrella class of tagged image file formats with randomly embedded tags and up to 24-bit color. Non-compressive.

 • .bmp - a non-compressive bit-mapped format in which individual pixel values can easily be extracted. In addition there are varieties of color coordinates used for “color separation,” such as HSI (Hue, Saturation, Intensity), or RGB (Red, Green, Blue), CMY, etc. 

But regardless of the sensor properties and coding format used, ultimately the image data must be represented numerically pixel by pixel. Typically this involves the conversion (e.g. by a tool such as xv) of the various compressed formats into .bmp, with an embedded header of formatting data. The total number of independent pixels in an image array determines the spatial resolution of the image. 

Independent of this is the grey-scale (or color) resolution of the image, which is determined by the number of bits of information specified for each pixel. These separate dimensions are illustrated in the following family of images, showing the effects of differing quantization accuracies for spatial and luminance information. It is typical for a monochromatic (“black & white”) image to have resolution of 8 bits/pixel. 

This creates 256 different possible intensity values for each pixel, from black (0) to white (255), with all shades of grey in between. A full-color image may be quantized to this depth in each of the three color planes, requiring a total of 24 bits per pixel. However, it is common to represent color more coarsely or even to combine luminance and chrominance information in such a way that their total information is only 8 or 12 bits/pixel.

Processing of Image 

Low level Process: These involve primitive operations such as image processing to reduce noise, contrast enhancement and image sharpening. These kinds of processes are characterized by fact the both inputs and output are images.

Mid-level Image Processing: It involves tasks like segmentation, description of those objects to reduce them to a form suitable for computer processing, and classification of individual objects. The inputs to the process are generally images but outputs are attributes extracted from images. 

High level Processing: It involves “making sense” of an ensemble of recognized objects, as in image analysis, and performing the cognitive functions normally associated with vision. 

Low-level vision

• Edge, corner, feature detection
• Stereo reconstruction
• Structure from motion, optical flow

Mid-level vision

• Texture
• Segmentation and grouping
• Illumination 

Segmentation and grouping

Computer vision is a field of computer science that enables computers to identify and process objects in videos and images just the way we humans do. We segment i.e. divide the images into regions of different colors which helps in distinguishing an object from the other at a finer level. Segmentation is the process of segmenting the image pixels into their respective classes. For example, in the figure above, the cat is associated with yellow color; hence all the pixels related to the cat are colored yellow. Multiple objects of the same class are considered as a single entity and hence represented with the same color.

High-level vision

• Tracking
• Specific object recognition
• Category-level object recognition
• Applications

Image or Object Detection is a computer technology that processes the image and detects objects in it. When there is a single object present in an image, we use image localization technique to draw a bounding box around that object. In the case of object detection, it provides labels along with the bounding boxes; hence we can predict the location as well as the class to which each object belongs.