Object Recognition Question & Answers July 7, 2021 By WatElectronics This article lists 100+ Object Recognition MCQs for engineering students. All the Object Recognition Questions & Answers given below includes solution and link wherever possible to the relevant topic. Rational function tester will not recognize the objects with the help of look and feel, but it finds out the object with the help of properties and its values. When we execute a particular test will get a log file and that log file shows recognition failures and warnings. Object recognition will be divided into two parts, it finds outs the object based on the recognition weights and recognition thresholds. Object detection and object recognition are some of the common computer vision problems which deal with identifying and locating objects of certain classes in the image. There are three types of recognition are there in artificial intelligence they are content-based image retrieval, biometric identification, and handwriting recognition. The vision recognizes the activities and also objects. The telephone directory assistance is an application of the speech recognition domain. The model database, hypothesizer, feature detector, and hypothesis verifier are the components of an object recognition system. The Human and computer interface face recognition system is used in biometric identification, human and computer interfaces. In a face recognition system, the complications occur due to variations in facial expressions, pose, illumination, etc. 1). How many types of recognition are there in artificial intelligence? One Two Three Four None Hint 2). The vision recognizes the ______ Activities Object Both a and b None of the above None Hint 3). The random variables are of _________ types One Two Three Four None Hint 4). _________ is an application of document image analysis Optical character recognition Junk mail filtering Information extraction All of the above None Hint 5). _________ is an application of the speech recognition domain Optical character recognition Junk mail filtering Information extraction Telephone directory assistance None Hint 6). What is the standard form of ALPR? Automatic License Plate Recognition Automatic License Plate Reader License Plate Recognition None of the above None Hint 7). _________ are the components of object recognition system Model database, hypothesizer Feature detector, hypothesis verifier Both a and b None of the above None Hint 8). The face recognition system used in _____ Biometric identification Human and computer interface Both a and b None of the above None Hint 9). In a face recognition system the complications occur due to variations in ________ Facial expressions Pose Illumination All of the above None Hint 10). What is the standard form of ANPR? Automatic Number Plate Recognition Automatic Number Plate Reader License Plate Recognition None of the above None Hint 11). The facial recognition uses _______ techniques Facial geometry Facial thermogram Skin pattern recognition, smile All of the above None Hint 12). Which one is a fingerprint matching technique? Pattern matching Minutiae-based matching Both a and b None of the above None Hint 13). ________ is an application of the medical domain Optical character recognition Computer-aided diagnosis Fruit sorting None of the above None Hint 14). ________ is an application of the remote sensing Optical character recognition Computer-aided diagnosis Fruit sorting Forecasting remote yield None Hint 15). What is the standard form of LPR? License Plate Reader Automatic License Plate Reader License Plate Recognition None of the above None Hint 16). What is the standard form of AVI? Automatic Volume Identification Automatic Vehicle Identification Automatic Voice Identification None of the above None Hint 17). What is the standard form of MLPR? Mobile Plate Recognition Mobile License Plate Recognition Mobile License Plate Reader None of the above None Hint 18). __________ are the applications of an object recognition Driverless cars Medical image processing Monitoring and surveillance All of the above None Hint 19). ________ can be represented by using empirical frequency distributions or histograms Colors Texture Both a and b None of the above None Hint 20). For studying object recognition ___________ learning provides a framework Supervised Unsupervised Both a and b None of the above None Hint 21). How many approaches are there to perform object recognition using deep learning? One Two Three Four None Hint 22). The segmentations are of ____ types One Two Three Four None Hint 23). __________ are the examples of object detection in real-time Tracking objects People counting Person detection All of the above None Hint 24). __________ are the difficulties in object recognition under varied circumstances Lighting, rotation, positioning Mirroring, occlusion, scale Both a and b None of the above None Hint 25). _________ are the main tasks in object recognition Classification, tagging Detection, segmentation Both a and b None of the above None Hint Object Recognition MCQs for Quiz 26). What is the standard form of CNN? Computer Neural Network Computer Network Neural Convolutional Neural Network None of the above None Hint 27). Which one is an instance-based method of object recognition? Decision stump Random forest K-nearest neighbor None of the above None Hint 28). Which one comes under the decision tree learning method of object recognition? Bayesian belief network Random forest Linear discriminant analysis None of the above None Hint 29). Which one comes under the bayesian method of object recognition? Bayesian belief network Decision stump Random forest None of the above None Hint 30). What is the standard form of RBF? Radial Basis Fraction Radial Basis Function Radial Base Fraction None of the above None Hint 31). What is the standard form of LDA? Linear Deep Learning Analysis Linear Determinant Analysis Linear Discriminant Analysis None of the above None Hint 32). Which one comes under clustering methods of object recognition? K-means Expectation maximization Both a and b None of the above None Hint 33). What is the standard form of SOM? Self Organizing Map Simple Organizing Map Self Organizing Machine None of the above None Hint 35). __________ comes under artificial neural network Perception Backpropagation Hopfield network All of the above None Hint 36). ________ are the deep learning methods of object recognition Restricted Boltzman Machine Deep belief networks Convolutional network, and stacked autoencoder All of the above None Hint 37). What is the standard form of RBM? Regional Boltzman Machine Restricted Boltzman Machine Radial Boltzman Machine None of the above None Hint 38). What is the standard form of LVQ? Linear Variant Quantization Linear Vector Quantization Non-linear Variant Quantization None of the above None Hint 39). What is the standard form of DBN? Deep Belief Networks Deep Boltzman Networks Discriminant Belief Networks None of the above None Hint 40). What is the standard form of HMM? Hidden Markov Model Hidden Markov Machine Hidden Machine Model None of the above None Hint 41). Which one is a type of neural network? Bayesian networks Linear networks Probabilistic networks All of the above None Hint 42). How many layers does a linear network have? One Two Three Four None Hint 43). _________ are the commonly used predictive data mining methods Decision trees, logistic regression Artificial neural networks, support vector machines Naive Bayes, Bayesian network, k nearest neighbor All of the above None Hint 44). __________ is an example of local discovering algorithm Naive Bayes Tree augmented naive Bayes Semi interleaved HITON PC All of the above None Hint 45). _________ are the examples of hybrid structure learning algorithms Max-min hill climbing Naive Bayes Tree augmented naive Bayes All of the above None Hint 46). ___________ are the example of constraint-based structure learning algorithms Grow shrink Hill climbing Tabu search All of the above None Hint 47). ___________ are the example of score-based structure learning algorithms Naive Bayes Hill climbing Both a and b None of the above None Hint 48). __________ are the popular inference methods Clique tree propagation Variable elimination Recursive conditioning All of the above None Hint 49). _______ are the Bayesian networks inference Parameter learning Structure learning Deducing unobserved variables All of the above None Hint 50). __________ are the examples of generative models Naive Bayes clasifier SVM Boosted decision trees All of the above None Hint Object Recognition MCQs for Students 51). __________ are the examples of discriminative models Naive bayes clasifier SVM Bayesian network All of the above None Hint 52). ____________ are the typical associative classification methods CBA CMAR CPAR All of the above None Hint 53). What is the standard form of CMAR? Classification Based on Association Rules Classification Based on Multiple Association Rules Classification Based on Predictive Association Rules None of the above None Hint 54). What is the standard form of CPAR? Classification Based on Predictive Association Recognition Classification Based on Periodic Association Rules Classification Based on Predictive Association Rules None of the above None Hint 55). Which one is a deterministic algorithm? SVM Neural network Both a and b None of the above None Hint 56). The neural networks are _________ Relatively old Non-deterministic algorithm Easy to learn All of the above None Hint 57). The SVM is __________ Relatively new concept Deterministic algorithm Hard to learn All of the above None Hint 58). _________ are the common applications of SVM Face detection Handwriting recognition Bioinformatics All of the above None Hint 59). The mathematical portion used in bioinformatics are ______ Matrices, differentiation/integration Biostatistics Complex mathematics functions All of the above None Hint 60). ________ are the properties of SVM Duality, kernels Margin, convexity Sparseness All of the above None Hint 61). Which one is a non-deterministic algorithm? SVM Neural network Both a and b None of the above None Hint 62). The genetic algorithm composed of ___________ operators Reproduction Mutation Crossover All of the above None Hint 63). The advantages of genetic algorithms are ______ Easy to implement Easy to understand Good for noisy environments All of the above None Hint 64). The disadvantages of genetic algorithms are ______ Slower than some other methods Choosing fitness and encoding is difficult Takes a long time to find a near-optimal solution All of the above None Hint 65). The advantages of fuzzy logic are _________ Easy to analyze Low cost Easy to understand All of the above None Hint 66). The disadvantages of fuzzy logic are _________ Not stable Complex to design Only provides a crude sizing All of the above None Hint 67). The advantages of artificial neural network are ________ Powerful Easy to use Alter to unknown conditions All of the above None Hint 68). The disadvantages of artificial neural network are ________ Large complexity of network structure Difficult to know how many layers and neurons are necessary Learning can be slow All of the above None Hint 69). The configuration of fuzzy logic consists of _______ modules Knowledge base Decision-making logic Defuzzification interface, fuzzification interface All of the above None Hint 70). An artificial intelligence used in _________ Communication Transportation Integrated applications All of the above None Hint 71). __________ are the applications of face detection Webcams that tracks the user Banking using ATM Biometrics/access control All of the above None Hint 72). What is the standard form of FRCNN? First Convolutional Neural Network First Region Convolutional Neural Network Faster Region Convolutional Neural Network None of the above None Hint 73). What is the standard form of SSD? Single Shot Detector Simple Shot Detector Single Shot Multibox Detector None of the above None Hint 74). What is the standard form of SVMs? Single Vector Machines Simple Vector Machines Support Vector Machines None of the above None Hint 75). _______ are the machine learning techniques SVM machine learning model Bag of words model Viola jones algorithm All of the above None Hint Object Recognition MCQs for Interviews 76). What is the standard form of YOLO? You Only Look Once You Once Look Only You Look Once None of the above None Hint 77). Which one is an application of neural networks? Data validation Risk management Both a and b None of the above None Hint 78). In __________ the decision nodes are represented Squares Circles Triangles Rectangle None Hint 79). In __________ the chance nodes are represented Circles Triangles Rectangle Squares None Hint 80). In __________ the end nodes are represented Circles Triangles Rectangle Squares None Hint 81). In closed classes _________ objects are available One Two Three Four None Hint 82). In parsing ________ states are available One Two Three Four None Hint 83). Which method deals with a pattern? Decision theoretic method Structure method Both a and b None of the above None Hint 84). The pattern recognition task consists of __________ steps One Two Three Four None Hint 85). The classifiers categorized into ______ methods One Two Three Four None Hint 86). __________ are the structural methods Matching shape numbers String matching Syntactic method All of the above None Hint 87). What are the disadvantages of matching shape numbers? Mirror problem intensity b) c)d Color is unable to recognize Cannot use for a hallow structure All of the above None Hint 88). Which is a method of object recognition? Decision tree learning Bayesian Kernel methods All of the above None Hint 89). The advantages of decision tree regression are _______ Very easy to interpret or visualize Works on both non-linear and linear problems No need to do feature scaling All of the above None Hint 90). The random forest regression are ________ Very powerful Very accurate Very good performance for both non-linear and linear problems All of the above None Hint 91). The SVR _________ Works very well on non-linear problems Can be easily adapted Not biased by outliers All of the above None Hint 92). The polynomial regression _________ Works on any size of data Works best for non-linear problems Both a and b None of the above None Hint 93). The linear regression _____________ Works with almost any kind of dataset Gives quite good information about the features Both a and b None of the above None Hint 94). The advantages of face detection are ________ Full automation High accuracy rates Improvement of security level All of the above None Hint 95). The disadvantages of SVR are ________ Not a familiar model Quiet difficult to understand Both a and b None of the above None Hint 96). The disadvantages of face detection are ______ Lower processing speed Surveillance angle Image size and quality All of the above None Hint 97). The disadvantages of decision tree regression are _______ Poor results on small datasets Overfitting can easily occur Both a and b None of the above None Hint 98). The template matching is sensitive to ______ Noise Occlusions Both a and b None of the above None Hint 99). The pixel-level template matching is of _______ type One Two Three Four None Hint 100). __________ are the template matching applications 3D reconstruction Motion detection Object recognition All of the above None Hint Read more about Face Recognition Time's up