The document discusses machine vision and image matching. It begins with definitions of image matching as the process of geometrically positioning two images so their pixels represent the same physical areas. It describes extracting local invariant features from images using methods like Scale Invariant Feature Transform (SIFT) to find correspondences between images for tasks like object recognition and panorama creation despite variations in lighting, viewpoint and scale. SIFT extracts key points from images and represents each with a 128-element feature vector for robust matching between images.
1. Unsupervised learning digunakan untuk pengelompokkan data tanpa label melalui clustering.
2. K-means clustering dan hierarchical clustering adalah dua pendekatan utama clustering.
3. Pemilihan parameter seperti jumlah cluster pada k-means mempengaruhi akurasi hasil clustering.
1. The document discusses machine vision techniques including image filtering in the frequency domain and wavelet transforms. It provides details on Fourier transforms, common filters like low pass and high pass, and compares Fourier and wavelet transforms.
2. Fourier transforms allow filtering images by manipulating the image's frequency spectrum but do not provide time information. Wavelet transforms analyze images based on frequency and time, providing advantages over Fourier transforms for non-stationary signals.
3. Common filters discussed are ideal, Butterworth, and Gaussian filters for both low pass and high pass. Examples show the effects of applying these filters to an image. Discrete wavelet transforms provide an efficient method to decompose signals into different frequency bands.
The document discusses machine vision and image matching. It begins with definitions of image matching as the process of geometrically positioning two images so their pixels represent the same physical areas. It describes extracting local invariant features from images using methods like Scale Invariant Feature Transform (SIFT) to find correspondences between images for tasks like object recognition and panorama creation despite variations in lighting, viewpoint and scale. SIFT extracts key points from images and represents each with a 128-element feature vector for robust matching between images.
1. Unsupervised learning digunakan untuk pengelompokkan data tanpa label melalui clustering.
2. K-means clustering dan hierarchical clustering adalah dua pendekatan utama clustering.
3. Pemilihan parameter seperti jumlah cluster pada k-means mempengaruhi akurasi hasil clustering.
1. The document discusses machine vision techniques including image filtering in the frequency domain and wavelet transforms. It provides details on Fourier transforms, common filters like low pass and high pass, and compares Fourier and wavelet transforms.
2. Fourier transforms allow filtering images by manipulating the image's frequency spectrum but do not provide time information. Wavelet transforms analyze images based on frequency and time, providing advantages over Fourier transforms for non-stationary signals.
3. Common filters discussed are ideal, Butterworth, and Gaussian filters for both low pass and high pass. Examples show the effects of applying these filters to an image. Discrete wavelet transforms provide an efficient method to decompose signals into different frequency bands.
Warna dari sebuah obyek dipengaruhi oleh interaksi antara cahaya dan material obyek, serta sistem penglihatan manusia. Beberapa faktor yang mempengaruhi warna antara lain pemantulan, penyerapan, dan pembelokan cahaya oleh material obyek, serta sensitivitas reseptor mata manusia terhadap panjang gelombang cahaya. Representasi warna dalam ruang warna seperti RGB dan CIE XYZ memungkinkan standarisasi persepsi warna.
This document provides an overview of machine vision applications including content-based image retrieval and face recognition. It discusses how content-based image retrieval systems work by extracting image features, calculating distances between images, and returning similar images from a database based on a query image. Examples of content-based image retrieval systems and the features they use are described. The document also covers face detection and recognition techniques, including the use of eigenfaces which represent faces as locations in a lower-dimensional space.
This document provides an overview of image matching techniques. It defines image matching as geometrically aligning two images so corresponding pixels represent the same scene region. Key aspects covered include detecting invariant local features, describing features in a scale and rotation invariant way using SIFT, and matching features between images. SIFT is highlighted as an extraordinarily robust technique that can handle various geometric and illumination changes. Feature matching is used in many computer vision applications such as image alignment, 3D reconstruction, and object recognition.
This document discusses unsupervised machine learning techniques for clustering unlabeled data. It covers k-means clustering, which partitions data into k groups based on minimizing distance between points and cluster centroids. It also discusses agglomerative hierarchical clustering, which successively merges clusters based on their distance. As an example, it shows hierarchical clustering of texture images from five classes to group similar textures.
This document provides an overview of pattern recognition and supervised learning for machine vision. It discusses what pattern recognition is, examples of pattern recognition applications, the basic steps in a pattern recognition system including data acquisition, preprocessing, feature extraction, supervised/unsupervised learning, and post-processing. For supervised learning, it describes the process of inferring functions from labeled training data. It also provides an example of using multiple features and decision boundaries for texture classification of images.
This document provides an overview of texture analysis techniques in machine vision. It discusses both structural and statistical approaches to texture analysis. Structural approaches attempt to model textures as repeating patterns of texture elements, while statistical approaches characterize textures using measures computed from pixel intensities alone. Specific statistical techniques covered include local binary patterns (LBP), gray-level co-occurrence matrices (GLCM), Laws texture energy measures, Fourier power spectrum, and wavelet texture descriptors. The document also discusses how these various texture features can be used for texture segmentation.
This document discusses various shape features that can be used for machine vision and image segmentation. It covers thresholding techniques, identifying object boundaries using chain codes and Fourier descriptors, and describing regions using basic descriptors like area and perimeter or moment invariants. Segmentation is described as an important but difficult task, and thresholding, discontinuities and region similarity are presented as common segmentation approaches. Examples are provided to illustrate different shape feature extraction methods.
This document provides an overview of feature detection techniques in machine vision, including edge detection, the Canny edge detector, interest points, and the Harris corner detector. It describes how edge detection works by finding discontinuities in images using masks and correlation. It explains that the Canny edge detector is an optimal method that uses Gaussian smoothing and non-maximum suppression. Interest points are localized features useful for applications like image alignment, and the Harris corner detector computes gradients to find locations with dominant directions, identifying corners.
This document provides an overview of image filtering in the frequency domain and introduces the wavelet transform. It discusses Fourier transforms and how they can be used to filter images. Specifically, it describes:
1) How low-pass filters smooth images by removing high frequency components, while high-pass filters sharpen images by removing low frequencies.
2) Common low-pass filters like ideal, Butterworth, and Gaussian filters and how their transfer functions are defined.
3) Examples of filtering an image with different low-pass filters to smooth or remove noise.
4) The limitations of the Fourier transform in analyzing non-stationary signals and how the wavelet transform provides time-frequency localization.
This document provides an overview of image filtering techniques in the spatial domain. It discusses smoothing filters using averaging and Gaussian weighting. It introduces first derivative filters like Sobel operators that detect edges, and second derivative filters like the Laplacian that are useful for sharpening. The Laplacian highlights edges by finding the second spatial derivative. Sharpening is done by subtracting the Laplacian from the original image. Variations are discussed.
This document provides an overview of light, color, and human color perception. It discusses that color is a psychological property resulting from light interacting with our visual system. The physics of light is described in terms of wavelength. Human color vision involves three types of cones that differ in photopigment sensitivity. Color can be represented using models like RGB, CIE XYZ, and HSV. Computer vision applications make use of color through techniques like color histograms, skin detection, and image segmentation.
The document provides an overview of the human visual system and digital cameras. It discusses the key components of the human eye, including the cornea, sclera, choroid, lens, and retina. It also describes how images are formed on the retina through the lens and light receptors. For digital cameras, the document outlines the basic components and image formation process, including the aperture, optical system, and imaging sensor. It also provides equations to convert between camera and image plane coordinates.
This document provides an overview of an introduction to machine vision course. The course introduces concepts of machine vision including image formation and filtering. It addresses machine vision techniques such as feature detection, extraction, and pattern recognition. Students will explore applications and learn about enabling technologies. The course involves assignments, midterm and final exams to assess learning outcomes including understanding, applying, analyzing, evaluating, and designing approaches related to machine vision. Related fields, optical illusions, sample applications, software, and resources are also discussed.
Dokumen tersebut membahas tentang tugas dan prosedur kerja operator telepon di hotel. Operator telepon bertugas menjawab telepon masuk, menyambungkan panggilan ke kamar tamu, mencatat pesan untuk tamu, dan menangani permintaan wake up call. Hal penting yang perlu diperhatikan meliputi menguasai alfabet internasional, tersenyum saat berbicara, mengecek data tamu secara akurat sebelum menyambungkan panggilan untuk memastikan privasi.
This document provides information on standard operating procedures for telephone operators in a hotel front office. It discusses telephone courtesy, how to connect phone calls to rooms, restaurants, and other departments. It emphasizes the importance of getting names, taking messages, and handling wake up call requests. The document is adapted from the book "Professional front office management" and aims to help recognize standard procedures in hotel front office departments.
This document provides standard operating procedures for various uniformed services roles in a hotel front office, including greetings, forms used, sample conversations, and references. It outlines the roles of bell services, doormen, airport representatives, concierge staff, and discusses procedures for handling guest luggage, renting vehicles, and more. Standard greetings are provided for new and returning guests. Required forms and documentation for tasks are also listed.
Dokumen ini membahas tentang seragam dan tanggung jawab bagian Uniform of Service di hotel, yang bervariasi bergantung pada ukuran dan klasifikasi hotel. Staff di bagian ini harus memperhatikan keterampilan berkomunikasi, pengetahuan tentang daerah sekitar, dan mengecek deposit tamu untuk penyewaan kendaraan. Kemampuan komunikasi yang baik sangat penting untuk berinteraksi dengan tamu.
Warna dari sebuah obyek dipengaruhi oleh interaksi antara cahaya dan material obyek, serta sistem penglihatan manusia. Beberapa faktor yang mempengaruhi warna antara lain pemantulan, penyerapan, dan pembelokan cahaya oleh material obyek, serta sensitivitas reseptor mata manusia terhadap panjang gelombang cahaya. Representasi warna dalam ruang warna seperti RGB dan CIE XYZ memungkinkan standarisasi persepsi warna.
This document provides an overview of machine vision applications including content-based image retrieval and face recognition. It discusses how content-based image retrieval systems work by extracting image features, calculating distances between images, and returning similar images from a database based on a query image. Examples of content-based image retrieval systems and the features they use are described. The document also covers face detection and recognition techniques, including the use of eigenfaces which represent faces as locations in a lower-dimensional space.
This document provides an overview of image matching techniques. It defines image matching as geometrically aligning two images so corresponding pixels represent the same scene region. Key aspects covered include detecting invariant local features, describing features in a scale and rotation invariant way using SIFT, and matching features between images. SIFT is highlighted as an extraordinarily robust technique that can handle various geometric and illumination changes. Feature matching is used in many computer vision applications such as image alignment, 3D reconstruction, and object recognition.
This document discusses unsupervised machine learning techniques for clustering unlabeled data. It covers k-means clustering, which partitions data into k groups based on minimizing distance between points and cluster centroids. It also discusses agglomerative hierarchical clustering, which successively merges clusters based on their distance. As an example, it shows hierarchical clustering of texture images from five classes to group similar textures.
This document provides an overview of pattern recognition and supervised learning for machine vision. It discusses what pattern recognition is, examples of pattern recognition applications, the basic steps in a pattern recognition system including data acquisition, preprocessing, feature extraction, supervised/unsupervised learning, and post-processing. For supervised learning, it describes the process of inferring functions from labeled training data. It also provides an example of using multiple features and decision boundaries for texture classification of images.
This document provides an overview of texture analysis techniques in machine vision. It discusses both structural and statistical approaches to texture analysis. Structural approaches attempt to model textures as repeating patterns of texture elements, while statistical approaches characterize textures using measures computed from pixel intensities alone. Specific statistical techniques covered include local binary patterns (LBP), gray-level co-occurrence matrices (GLCM), Laws texture energy measures, Fourier power spectrum, and wavelet texture descriptors. The document also discusses how these various texture features can be used for texture segmentation.
This document discusses various shape features that can be used for machine vision and image segmentation. It covers thresholding techniques, identifying object boundaries using chain codes and Fourier descriptors, and describing regions using basic descriptors like area and perimeter or moment invariants. Segmentation is described as an important but difficult task, and thresholding, discontinuities and region similarity are presented as common segmentation approaches. Examples are provided to illustrate different shape feature extraction methods.
This document provides an overview of feature detection techniques in machine vision, including edge detection, the Canny edge detector, interest points, and the Harris corner detector. It describes how edge detection works by finding discontinuities in images using masks and correlation. It explains that the Canny edge detector is an optimal method that uses Gaussian smoothing and non-maximum suppression. Interest points are localized features useful for applications like image alignment, and the Harris corner detector computes gradients to find locations with dominant directions, identifying corners.
This document provides an overview of image filtering in the frequency domain and introduces the wavelet transform. It discusses Fourier transforms and how they can be used to filter images. Specifically, it describes:
1) How low-pass filters smooth images by removing high frequency components, while high-pass filters sharpen images by removing low frequencies.
2) Common low-pass filters like ideal, Butterworth, and Gaussian filters and how their transfer functions are defined.
3) Examples of filtering an image with different low-pass filters to smooth or remove noise.
4) The limitations of the Fourier transform in analyzing non-stationary signals and how the wavelet transform provides time-frequency localization.
This document provides an overview of image filtering techniques in the spatial domain. It discusses smoothing filters using averaging and Gaussian weighting. It introduces first derivative filters like Sobel operators that detect edges, and second derivative filters like the Laplacian that are useful for sharpening. The Laplacian highlights edges by finding the second spatial derivative. Sharpening is done by subtracting the Laplacian from the original image. Variations are discussed.
This document provides an overview of light, color, and human color perception. It discusses that color is a psychological property resulting from light interacting with our visual system. The physics of light is described in terms of wavelength. Human color vision involves three types of cones that differ in photopigment sensitivity. Color can be represented using models like RGB, CIE XYZ, and HSV. Computer vision applications make use of color through techniques like color histograms, skin detection, and image segmentation.
The document provides an overview of the human visual system and digital cameras. It discusses the key components of the human eye, including the cornea, sclera, choroid, lens, and retina. It also describes how images are formed on the retina through the lens and light receptors. For digital cameras, the document outlines the basic components and image formation process, including the aperture, optical system, and imaging sensor. It also provides equations to convert between camera and image plane coordinates.
This document provides an overview of an introduction to machine vision course. The course introduces concepts of machine vision including image formation and filtering. It addresses machine vision techniques such as feature detection, extraction, and pattern recognition. Students will explore applications and learn about enabling technologies. The course involves assignments, midterm and final exams to assess learning outcomes including understanding, applying, analyzing, evaluating, and designing approaches related to machine vision. Related fields, optical illusions, sample applications, software, and resources are also discussed.
Dokumen tersebut membahas tentang tugas dan prosedur kerja operator telepon di hotel. Operator telepon bertugas menjawab telepon masuk, menyambungkan panggilan ke kamar tamu, mencatat pesan untuk tamu, dan menangani permintaan wake up call. Hal penting yang perlu diperhatikan meliputi menguasai alfabet internasional, tersenyum saat berbicara, mengecek data tamu secara akurat sebelum menyambungkan panggilan untuk memastikan privasi.
This document provides information on standard operating procedures for telephone operators in a hotel front office. It discusses telephone courtesy, how to connect phone calls to rooms, restaurants, and other departments. It emphasizes the importance of getting names, taking messages, and handling wake up call requests. The document is adapted from the book "Professional front office management" and aims to help recognize standard procedures in hotel front office departments.
This document provides standard operating procedures for various uniformed services roles in a hotel front office, including greetings, forms used, sample conversations, and references. It outlines the roles of bell services, doormen, airport representatives, concierge staff, and discusses procedures for handling guest luggage, renting vehicles, and more. Standard greetings are provided for new and returning guests. Required forms and documentation for tasks are also listed.
Dokumen ini membahas tentang seragam dan tanggung jawab bagian Uniform of Service di hotel, yang bervariasi bergantung pada ukuran dan klasifikasi hotel. Staff di bagian ini harus memperhatikan keterampilan berkomunikasi, pengetahuan tentang daerah sekitar, dan mengecek deposit tamu untuk penyewaan kendaraan. Kemampuan komunikasi yang baik sangat penting untuk berinteraksi dengan tamu.
1. HTMN6014 - Front Office Operation
LECTURE NOTES
Week ke - 4
Managing Guest Reservation-1
2. HTMN6014 - Front Office Operation
LEARNING OUTCOMES
LO 1: Classify the knowledge of Front Office Department.
LO 2: Perform professional look and correct standard operation procedures of Front Office
operations.
OUTLINE MATERI :
1. Sumber Reservasi dan Jenis Harga.
2. Room Plans
3. Beberapa contoh tipe kamar
3. HTMN6014 - Front Office Operation
ISI MATERI
Sumber Reservasi dan Jenis Harga.
Jenis harga yang umumnya berlaku adalah :
1. Publish rate: nama lainnya adalah rack rate. Harga normal yang berlaku di hari itu.
Publish rate biasanya ditemukan di website hotel. Publish rate dapat berubah-ubah sesuai
ketersediaan kamar, harga nya dapat menjadi tinggi saat peak season. Harga publish rate
ada yang Room Only, ada yang Include Breakfast.
2. Company rate: harga khusus untuk perusahaan yang bekerjasama dengan hotel.
3. Staff rate: harga kamar yang diberikan khusus untuk staff yang bekerja untuk hotel itu
ataupun grup dari hotel itu. Sebagai contoh: staff dari The Westin Hotel, memesan kamar
untuk menginap di Aloft Hotel menggunakan Staff Rate.
Room Plans
Beberapa istilah dalam Room Plans (penawaran kamar dan makanan):
• European Plan (EP) : Harga kamar saja tanpa sarapan
• American Plan (AP) : Harga kamar ditambah dengan sarapan, makan siang & makan
malam.
• Modified American Plan (AP) : Harga kamar ditambah sarapan dengan makan siang atau
sarapan dengan makan malam.
• Continental Plan (CP) : Harga kamar dengan sarapan ala kontinental.
• Bed and Breakfast: Harga kamar dengan sarapan ala Inggris.
4. HTMN6014 - Front Office Operation
Untuk jenis sarapannya adalah sebagai berikut:
1. English Breakfast:
• Juices, buah potong dan buah kukus, yoghurt
• Cereals
• Beberapa jenis telur untuk sarapan.
• Beberapa jenis daging secperti bacon, sosis.
• Kentang hash brown dan grilled tomato
• Roti panggang dan beberapa jenis roti dengan butter.
• Selai serta teh dan kopi
2. Continental Breakfast:
• Juices, buah-buahan, yoghurt
• beberapa jenis roti dengan butter
• Selai, madu
• Potongan daging dingin
• Keju, teh dan kopi
3. American Breakfast:
• English Breakfast dengan tambahan waffles atau pancakes dengan sirup maple.
• Kopi.
5. HTMN6014 - Front Office Operation
Beberapa contoh tipe kamar
• Adjacent Room : Dua kamar berhadapan dalam satu koridor.
• Adjoining Room : Dua kamar bersebelahan di koridor yang sama.
• Cabana : Kamar dengan sofa dan biasanya terletak di sebelah kolam renang atau pantai.
• Connecting Room : Dua kamar bersebelahan yang dihubungkan dengan pintu penghubung.
Kamar tipe ini cocok untuk tamu keluarga.
• Executive Room : Kamar Eksekutif yang biasanya didesain untuk pebisnis eksekutif.
• Studio : Kamar dengan tambahan sofa bed.
• Single Room : Kamar dengan tempat tidur untuk satu orang.
• Double Room : Kamar dengan satu tempat tidur besar, biasanya berukuran king size.
• Twin : Kamar dengan dua tempat tidur ukuran satu orang yang diletakkan terpisah.
• Triplet: Kamar dengan satu tempat tidur besar dengan tempat tidur ekstra yang dapat
dipindahkan.
6. HTMN6014 - Front Office Operation
Contoh Confirmation Letter:
Kebon Jeruk Raya Street, 27
Kebon Jeruk – Jakarta Barat 11530
Phone : (021) 53696969, 53696999, Fax. : (021) 530 – 0244
www.binushotel.com
Confirmation Number
Reservation Date
Booker Mr/ Mrs
Email & Phone
Dear Mr/Mrs........
(Booker’s name)
Warmest Greeting from Binus Hotel,
Further to your interest on room reservation at BINUS Hotel, we are pleased to confirm
you reservation as follow:
Guest’s Name :
Period of stay :
Room Type & Room Rate :
No. of Room/Guest/Children : ___Room /____Guest/___Children
Payment method :
Remarks :
Arrival flight : (if any)
Airport transfer : (if any)
Thank you for choosing BINUS HOTEL and we look forward to welcoming you to our hotel.
Sincerely Yours,
Name:
(Reservation Agent)
7. HTMN6014 - Front Office Operation
SIMPULAN
1. Pengetahuan mengenai jenis kamar dan jenis harga, wajib dimiliki oleh Reservation
Agent.
8. HTMN6014 - Front Office Operation
DAFTAR PUSTAKA
1. Robert H. Woods ... [et al.].. 2007. Professional front office management. 1st Books
Library. Upper Saddle River, New Jersey. ISBN:0131700693, chapter 10.
2. Andrews, Sudhir.2013. Hotel Front Office: A Training Manual.Mc Graw Hill Education.
ISBN: 1-25-900497-X, Chapter 15